Overview

Dataset statistics

Number of variables28
Number of observations111
Missing cells63
Missing cells (%)2.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory24.4 KiB
Average record size in memory225.2 B

Variable types

Numeric9
Categorical19

Alerts

airdate has constant value "2020-12-30" Constant
url has a high cardinality: 111 distinct values High cardinality
name has a high cardinality: 86 distinct values High cardinality
_embedded_show_url has a high cardinality: 70 distinct values High cardinality
_embedded_show_name has a high cardinality: 70 distinct values High cardinality
_embedded_show_premiered has a high cardinality: 52 distinct values High cardinality
_embedded_show_officialSite has a high cardinality: 63 distinct values High cardinality
_embedded_show_summary has a high cardinality: 62 distinct values High cardinality
_links_self_href has a high cardinality: 111 distinct values High cardinality
runtime is highly correlated with _embedded_show_runtime and 1 other fieldsHigh correlation
_embedded_show_runtime is highly correlated with runtime and 1 other fieldsHigh correlation
_embedded_show_averageRuntime is highly correlated with runtime and 1 other fieldsHigh correlation
season is highly correlated with number and 1 other fieldsHigh correlation
number is highly correlated with season and 1 other fieldsHigh correlation
runtime is highly correlated with _embedded_show_runtime and 1 other fieldsHigh correlation
_embedded_show_runtime is highly correlated with season and 3 other fieldsHigh correlation
_embedded_show_averageRuntime is highly correlated with runtime and 1 other fieldsHigh correlation
runtime is highly correlated with _embedded_show_runtime and 1 other fieldsHigh correlation
_embedded_show_runtime is highly correlated with runtime and 1 other fieldsHigh correlation
_embedded_show_averageRuntime is highly correlated with runtime and 1 other fieldsHigh correlation
name is highly correlated with airdateHigh correlation
_embedded_show_officialSite is highly correlated with _embedded_show_name and 12 other fieldsHigh correlation
_embedded_show_ended is highly correlated with _embedded_show_name and 7 other fieldsHigh correlation
_embedded_show_name is highly correlated with _embedded_show_officialSite and 13 other fieldsHigh correlation
summary is highly correlated with type and 1 other fieldsHigh correlation
_embedded_show_summary is highly correlated with _embedded_show_officialSite and 11 other fieldsHigh correlation
_embedded_show_genres is highly correlated with _embedded_show_officialSite and 9 other fieldsHigh correlation
type is highly correlated with _embedded_show_officialSite and 7 other fieldsHigh correlation
_embedded_show_type is highly correlated with _embedded_show_officialSite and 6 other fieldsHigh correlation
airdate is highly correlated with name and 15 other fieldsHigh correlation
airtime is highly correlated with _embedded_show_officialSite and 9 other fieldsHigh correlation
_embedded_show_status is highly correlated with _embedded_show_officialSite and 8 other fieldsHigh correlation
_embedded_show_premiered is highly correlated with _embedded_show_officialSite and 8 other fieldsHigh correlation
_embedded_show_url is highly correlated with _embedded_show_officialSite and 13 other fieldsHigh correlation
_embedded_show_language is highly correlated with _embedded_show_officialSite and 7 other fieldsHigh correlation
_embedded_show_dvdCountry is highly correlated with _embedded_show_officialSite and 6 other fieldsHigh correlation
airstamp is highly correlated with _embedded_show_officialSite and 7 other fieldsHigh correlation
id is highly correlated with name and 17 other fieldsHigh correlation
name is highly correlated with id and 21 other fieldsHigh correlation
season is highly correlated with name and 11 other fieldsHigh correlation
number is highly correlated with name and 9 other fieldsHigh correlation
type is highly correlated with id and 10 other fieldsHigh correlation
airtime is highly correlated with id and 14 other fieldsHigh correlation
airstamp is highly correlated with id and 18 other fieldsHigh correlation
runtime is highly correlated with name and 14 other fieldsHigh correlation
summary is highly correlated with name and 4 other fieldsHigh correlation
_embedded_show_id is highly correlated with id and 16 other fieldsHigh correlation
_embedded_show_url is highly correlated with id and 22 other fieldsHigh correlation
_embedded_show_name is highly correlated with id and 22 other fieldsHigh correlation
_embedded_show_type is highly correlated with id and 20 other fieldsHigh correlation
_embedded_show_language is highly correlated with id and 17 other fieldsHigh correlation
_embedded_show_genres is highly correlated with id and 19 other fieldsHigh correlation
_embedded_show_status is highly correlated with id and 14 other fieldsHigh correlation
_embedded_show_runtime is highly correlated with id and 17 other fieldsHigh correlation
_embedded_show_averageRuntime is highly correlated with name and 17 other fieldsHigh correlation
_embedded_show_premiered is highly correlated with id and 22 other fieldsHigh correlation
_embedded_show_ended is highly correlated with id and 14 other fieldsHigh correlation
_embedded_show_officialSite is highly correlated with id and 22 other fieldsHigh correlation
_embedded_show_weight is highly correlated with id and 18 other fieldsHigh correlation
_embedded_show_dvdCountry is highly correlated with airtime and 14 other fieldsHigh correlation
_embedded_show_summary is highly correlated with id and 20 other fieldsHigh correlation
_embedded_show_updated is highly correlated with id and 17 other fieldsHigh correlation
number has 2 (1.8%) missing values Missing
runtime has 8 (7.2%) missing values Missing
_embedded_show_runtime has 48 (43.2%) missing values Missing
_embedded_show_averageRuntime has 5 (4.5%) missing values Missing
url is uniformly distributed Uniform
_links_self_href is uniformly distributed Uniform
id has unique values Unique
url has unique values Unique
_links_self_href has unique values Unique
_embedded_show_weight has 8 (7.2%) zeros Zeros

Reproduction

Analysis started2022-05-10 02:23:35.785875
Analysis finished2022-05-10 02:24:08.540881
Duration32.76 seconds
Software versionpandas-profiling v3.2.0
Download configurationconfig.json

Variables

id
Real number (ℝ≥0)

HIGH CORRELATION
UNIQUE

Distinct111
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2051417.703
Minimum1945903
Maximum2324426
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1016.0 B
2022-05-09T21:24:08.626807image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1945903
5-th percentile1972359
Q11985598.5
median1996398
Q32070190.5
95-th percentile2312219.5
Maximum2324426
Range378523
Interquartile range (IQR)84592

Descriptive statistics

Standard deviation110853.8644
Coefficient of variation (CV)0.05403768537
Kurtosis0.8456988113
Mean2051417.703
Median Absolute Deviation (MAD)15989
Skewness1.520754123
Sum227707365
Variance1.228857925 × 1010
MonotonicityNot monotonic
2022-05-09T21:24:08.747542image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
21796151
 
0.9%
19880751
 
0.9%
19957521
 
0.9%
19953861
 
0.9%
19957471
 
0.9%
19957461
 
0.9%
19957451
 
0.9%
19957441
 
0.9%
19957431
 
0.9%
19957421
 
0.9%
Other values (101)101
91.0%
ValueCountFrequency (%)
19459031
0.9%
19585761
0.9%
19588691
0.9%
19644971
0.9%
19644981
0.9%
19702551
0.9%
19744631
0.9%
19753721
0.9%
19760521
0.9%
19760531
0.9%
ValueCountFrequency (%)
23244261
0.9%
23244251
0.9%
23181161
0.9%
23122221
0.9%
23122211
0.9%
23122201
0.9%
23122191
0.9%
23122181
0.9%
23122171
0.9%
23047041
0.9%

url
Categorical

HIGH CARDINALITY
UNIFORM
UNIQUE

Distinct111
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size1016.0 B
https://www.tvmaze.com/episodes/2179615/kontakty-1x32-kontakty-v-telefone-arsenia-popova-pavel-vola-ekaterina-varnava-ila-sobolev-edgard-zapasnyj
 
1
https://www.tvmaze.com/episodes/1988075/forever-love-1x24-episode-24
 
1
https://www.tvmaze.com/episodes/1995752/sanpa-luci-e-tenebre-di-san-patrignano-1x02-crescita
 
1
https://www.tvmaze.com/episodes/1995386/sanpa-luci-e-tenebre-di-san-patrignano-1x01-nascita
 
1
https://www.tvmaze.com/episodes/1995747/best-leftovers-ever-1x08-bland-to-flavor-bomb
 
1
Other values (106)
106 

Length

Max length145
Median length107
Mean length83.08108108
Min length58

Characters and Unicode

Total characters9222
Distinct characters40
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique111 ?
Unique (%)100.0%

Sample

1st rowhttps://www.tvmaze.com/episodes/2179615/kontakty-1x32-kontakty-v-telefone-arsenia-popova-pavel-vola-ekaterina-varnava-ila-sobolev-edgard-zapasnyj
2nd rowhttps://www.tvmaze.com/episodes/2001718/zona-komforta-s01-special-zona-komforta-osuitelno-specialnyj-vypusk
3rd rowhttps://www.tvmaze.com/episodes/1987721/passaziry-1x03-kirill
4th rowhttps://www.tvmaze.com/episodes/2095630/yi-nian-yong-heng-1x23-episode-23
5th rowhttps://www.tvmaze.com/episodes/1993659/7-days-of-romance-2x04-episode-4

Common Values

ValueCountFrequency (%)
https://www.tvmaze.com/episodes/2179615/kontakty-1x32-kontakty-v-telefone-arsenia-popova-pavel-vola-ekaterina-varnava-ila-sobolev-edgard-zapasnyj1
 
0.9%
https://www.tvmaze.com/episodes/1988075/forever-love-1x24-episode-241
 
0.9%
https://www.tvmaze.com/episodes/1995752/sanpa-luci-e-tenebre-di-san-patrignano-1x02-crescita1
 
0.9%
https://www.tvmaze.com/episodes/1995386/sanpa-luci-e-tenebre-di-san-patrignano-1x01-nascita1
 
0.9%
https://www.tvmaze.com/episodes/1995747/best-leftovers-ever-1x08-bland-to-flavor-bomb1
 
0.9%
https://www.tvmaze.com/episodes/1995746/best-leftovers-ever-1x07-fiesta-feast1
 
0.9%
https://www.tvmaze.com/episodes/1995745/best-leftovers-ever-1x06-welcome-to-the-neighborhood1
 
0.9%
https://www.tvmaze.com/episodes/1995744/best-leftovers-ever-1x05-down-home-to-uptown1
 
0.9%
https://www.tvmaze.com/episodes/1995743/best-leftovers-ever-1x04-pot-roast-to-french-toast1
 
0.9%
https://www.tvmaze.com/episodes/1995742/best-leftovers-ever-1x03-the-holidays1
 
0.9%
Other values (101)101
91.0%

Length

2022-05-09T21:24:08.873677image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
https://www.tvmaze.com/episodes/2179615/kontakty-1x32-kontakty-v-telefone-arsenia-popova-pavel-vola-ekaterina-varnava-ila-sobolev-edgard-zapasnyj1
 
0.9%
https://www.tvmaze.com/episodes/2017663/masrah-masr-8x09-a-day-that-passed1
 
0.9%
https://www.tvmaze.com/episodes/1987721/passaziry-1x03-kirill1
 
0.9%
https://www.tvmaze.com/episodes/2095630/yi-nian-yong-heng-1x23-episode-231
 
0.9%
https://www.tvmaze.com/episodes/1993659/7-days-of-romance-2x04-episode-41
 
0.9%
https://www.tvmaze.com/episodes/2096304/no-turning-back-romance-1x08-81
 
0.9%
https://www.tvmaze.com/episodes/2324425/unique-lady-2x13-episode-131
 
0.9%
https://www.tvmaze.com/episodes/2324426/unique-lady-2x14-episode-141
 
0.9%
https://www.tvmaze.com/episodes/1998603/unique-lady-2-1x13-episode-131
 
0.9%
https://www.tvmaze.com/episodes/1998605/unique-lady-2-1x14-episode-141
 
0.9%
Other values (101)101
91.0%

Most occurring characters

ValueCountFrequency (%)
e795
 
8.6%
-746
 
8.1%
s579
 
6.3%
/555
 
6.0%
t553
 
6.0%
o519
 
5.6%
a382
 
4.1%
w365
 
4.0%
i360
 
3.9%
p341
 
3.7%
Other values (30)4027
43.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter6307
68.4%
Decimal Number1281
 
13.9%
Other Punctuation888
 
9.6%
Dash Punctuation746
 
8.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e795
12.6%
s579
 
9.2%
t553
 
8.8%
o519
 
8.2%
a382
 
6.1%
w365
 
5.8%
i360
 
5.7%
p341
 
5.4%
m303
 
4.8%
d263
 
4.2%
Other values (16)1847
29.3%
Decimal Number
ValueCountFrequency (%)
1268
20.9%
2195
15.2%
0164
12.8%
9147
11.5%
5102
 
8.0%
496
 
7.5%
383
 
6.5%
782
 
6.4%
677
 
6.0%
867
 
5.2%
Other Punctuation
ValueCountFrequency (%)
/555
62.5%
.222
 
25.0%
:111
 
12.5%
Dash Punctuation
ValueCountFrequency (%)
-746
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin6307
68.4%
Common2915
31.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
e795
12.6%
s579
 
9.2%
t553
 
8.8%
o519
 
8.2%
a382
 
6.1%
w365
 
5.8%
i360
 
5.7%
p341
 
5.4%
m303
 
4.8%
d263
 
4.2%
Other values (16)1847
29.3%
Common
ValueCountFrequency (%)
-746
25.6%
/555
19.0%
1268
 
9.2%
.222
 
7.6%
2195
 
6.7%
0164
 
5.6%
9147
 
5.0%
:111
 
3.8%
5102
 
3.5%
496
 
3.3%
Other values (4)309
10.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII9222
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e795
 
8.6%
-746
 
8.1%
s579
 
6.3%
/555
 
6.0%
t553
 
6.0%
o519
 
5.6%
a382
 
4.1%
w365
 
4.0%
i360
 
3.9%
p341
 
3.7%
Other values (30)4027
43.7%

name
Categorical

HIGH CARDINALITY
HIGH CORRELATION
HIGH CORRELATION

Distinct86
Distinct (%)77.5%
Missing0
Missing (%)0.0%
Memory size1016.0 B
Episode 4
 
6
Episode 1
 
5
Episode 6
 
4
Episode 2
 
4
Episode 5
 
3
Other values (81)
89 

Length

Max length96
Median length79
Mean length17.65765766
Min length1

Characters and Unicode

Total characters1960
Distinct characters126
Distinct categories9 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique74 ?
Unique (%)66.7%

Sample

1st rowКОНТАКТЫ в телефоне Арсения Попова: Павел Воля, Екатерина Варнава, Илья Соболев, Эдгард Запашный
2nd rowЗона Комфорта. Ошуительно Специальный Выпуск
3rd rowКирилл
4th rowEpisode 23
5th rowEpisode 4

Common Values

ValueCountFrequency (%)
Episode 46
 
5.4%
Episode 15
 
4.5%
Episode 64
 
3.6%
Episode 24
 
3.6%
Episode 53
 
2.7%
Episode 133
 
2.7%
Episode 262
 
1.8%
Episode 32
 
1.8%
Episode 252
 
1.8%
Episode 142
 
1.8%
Other values (76)78
70.3%

Length

2022-05-09T21:24:09.130495image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
episode51
 
14.3%
18
 
2.2%
the8
 
2.2%
to8
 
2.2%
26
 
1.7%
46
 
1.7%
6
 
1.7%
and4
 
1.1%
64
 
1.1%
54
 
1.1%
Other values (218)251
70.5%

Most occurring characters

ValueCountFrequency (%)
245
 
12.5%
e147
 
7.5%
o115
 
5.9%
i96
 
4.9%
s94
 
4.8%
d80
 
4.1%
a72
 
3.7%
p65
 
3.3%
t55
 
2.8%
E54
 
2.8%
Other values (116)937
47.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter1282
65.4%
Uppercase Letter258
 
13.2%
Space Separator245
 
12.5%
Decimal Number131
 
6.7%
Other Punctuation32
 
1.6%
Dash Punctuation6
 
0.3%
Math Symbol2
 
0.1%
Close Punctuation2
 
0.1%
Open Punctuation2
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e147
 
11.5%
o115
 
9.0%
i96
 
7.5%
s94
 
7.3%
d80
 
6.2%
a72
 
5.6%
p65
 
5.1%
t55
 
4.3%
r49
 
3.8%
n47
 
3.7%
Other values (51)462
36.0%
Uppercase Letter
ValueCountFrequency (%)
E54
20.9%
F12
 
4.7%
D11
 
4.3%
H9
 
3.5%
T9
 
3.5%
A9
 
3.5%
О9
 
3.5%
B8
 
3.1%
К8
 
3.1%
В8
 
3.1%
Other values (33)121
46.9%
Decimal Number
ValueCountFrequency (%)
235
26.7%
126
19.8%
017
13.0%
414
 
10.7%
314
 
10.7%
69
 
6.9%
59
 
6.9%
84
 
3.1%
92
 
1.5%
71
 
0.8%
Other Punctuation
ValueCountFrequency (%)
,15
46.9%
:6
 
18.8%
.5
 
15.6%
?2
 
6.2%
!2
 
6.2%
&1
 
3.1%
/1
 
3.1%
Space Separator
ValueCountFrequency (%)
245
100.0%
Dash Punctuation
ValueCountFrequency (%)
-6
100.0%
Math Symbol
ValueCountFrequency (%)
|2
100.0%
Close Punctuation
ValueCountFrequency (%)
)2
100.0%
Open Punctuation
ValueCountFrequency (%)
(2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin1203
61.4%
Common420
 
21.4%
Cyrillic337
 
17.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
e147
 
12.2%
o115
 
9.6%
i96
 
8.0%
s94
 
7.8%
d80
 
6.7%
a72
 
6.0%
p65
 
5.4%
t55
 
4.6%
E54
 
4.5%
r49
 
4.1%
Other values (43)376
31.3%
Cyrillic
ValueCountFrequency (%)
а30
 
8.9%
и25
 
7.4%
е20
 
5.9%
л19
 
5.6%
о19
 
5.6%
н18
 
5.3%
р15
 
4.5%
т14
 
4.2%
в12
 
3.6%
с10
 
3.0%
Other values (41)155
46.0%
Common
ValueCountFrequency (%)
245
58.3%
235
 
8.3%
126
 
6.2%
017
 
4.0%
,15
 
3.6%
414
 
3.3%
314
 
3.3%
69
 
2.1%
59
 
2.1%
-6
 
1.4%
Other values (12)30
 
7.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII1612
82.2%
Cyrillic337
 
17.2%
None11
 
0.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
245
15.2%
e147
 
9.1%
o115
 
7.1%
i96
 
6.0%
s94
 
5.8%
d80
 
5.0%
a72
 
4.5%
p65
 
4.0%
t55
 
3.4%
E54
 
3.3%
Other values (58)589
36.5%
Cyrillic
ValueCountFrequency (%)
а30
 
8.9%
и25
 
7.4%
е20
 
5.9%
л19
 
5.6%
о19
 
5.6%
н18
 
5.3%
р15
 
4.5%
т14
 
4.2%
в12
 
3.6%
с10
 
3.0%
Other values (41)155
46.0%
None
ValueCountFrequency (%)
ų3
27.3%
å3
27.3%
æ1
 
9.1%
ø1
 
9.1%
É1
 
9.1%
á1
 
9.1%
ž1
 
9.1%

season
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION

Distinct11
Distinct (%)9.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean93.00900901
Minimum1
Maximum2020
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1016.0 B
2022-05-09T21:24:09.224392image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q32
95-th percentile22.5
Maximum2020
Range2019
Interquartile range (IQR)1

Descriptive statistics

Standard deviation420.4278015
Coefficient of variation (CV)4.520291163
Kurtosis18.10329264
Mean93.00900901
Median Absolute Deviation (MAD)0
Skewness4.447000831
Sum10324
Variance176759.5363
MonotonicityNot monotonic
2022-05-09T21:24:09.305573image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
177
69.4%
213
 
11.7%
20205
 
4.5%
54
 
3.6%
44
 
3.6%
72
 
1.8%
32
 
1.8%
81
 
0.9%
121
 
0.9%
311
 
0.9%
ValueCountFrequency (%)
177
69.4%
213
 
11.7%
32
 
1.8%
44
 
3.6%
54
 
3.6%
72
 
1.8%
81
 
0.9%
121
 
0.9%
141
 
0.9%
311
 
0.9%
ValueCountFrequency (%)
20205
 
4.5%
311
 
0.9%
141
 
0.9%
121
 
0.9%
81
 
0.9%
72
 
1.8%
54
 
3.6%
44
 
3.6%
32
 
1.8%
213
11.7%

number
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct42
Distinct (%)38.5%
Missing2
Missing (%)1.8%
Infinite0
Infinite (%)0.0%
Mean24.47706422
Minimum1
Maximum357
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1016.0 B
2022-05-09T21:24:09.422891image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median8
Q323
95-th percentile66.6
Maximum357
Range356
Interquartile range (IQR)20

Descriptive statistics

Standard deviation55.47031177
Coefficient of variation (CV)2.266215886
Kurtosis25.70483364
Mean24.47706422
Median Absolute Deviation (MAD)6
Skewness4.941825586
Sum2668
Variance3076.955488
MonotonicityNot monotonic
2022-05-09T21:24:09.532314image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
112
 
10.8%
29
 
8.1%
49
 
8.1%
58
 
7.2%
37
 
6.3%
67
 
6.3%
85
 
4.5%
135
 
4.5%
113
 
2.7%
233
 
2.7%
Other values (32)41
36.9%
ValueCountFrequency (%)
112
10.8%
29
8.1%
37
6.3%
49
8.1%
58
7.2%
67
6.3%
71
 
0.9%
85
4.5%
91
 
0.9%
101
 
0.9%
ValueCountFrequency (%)
3571
0.9%
3221
0.9%
3211
0.9%
891
0.9%
861
0.9%
711
0.9%
601
0.9%
591
0.9%
551
0.9%
531
0.9%

type
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Memory size1016.0 B
regular
109 
insignificant_special
 
1
significant_special
 
1

Length

Max length21
Median length7
Mean length7.234234234
Min length7

Characters and Unicode

Total characters803
Distinct characters14
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)1.8%

Sample

1st rowregular
2nd rowinsignificant_special
3rd rowregular
4th rowregular
5th rowregular

Common Values

ValueCountFrequency (%)
regular109
98.2%
insignificant_special1
 
0.9%
significant_special1
 
0.9%

Length

2022-05-09T21:24:09.643330image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-09T21:24:09.746022image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
regular109
98.2%
insignificant_special1
 
0.9%
significant_special1
 
0.9%

Most occurring characters

ValueCountFrequency (%)
r218
27.1%
a113
14.1%
e111
13.8%
g111
13.8%
l111
13.8%
u109
13.6%
i9
 
1.1%
n5
 
0.6%
s4
 
0.5%
c4
 
0.5%
Other values (4)8
 
1.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter801
99.8%
Connector Punctuation2
 
0.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r218
27.2%
a113
14.1%
e111
13.9%
g111
13.9%
l111
13.9%
u109
13.6%
i9
 
1.1%
n5
 
0.6%
s4
 
0.5%
c4
 
0.5%
Other values (3)6
 
0.7%
Connector Punctuation
ValueCountFrequency (%)
_2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin801
99.8%
Common2
 
0.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
r218
27.2%
a113
14.1%
e111
13.9%
g111
13.9%
l111
13.9%
u109
13.6%
i9
 
1.1%
n5
 
0.6%
s4
 
0.5%
c4
 
0.5%
Other values (3)6
 
0.7%
Common
ValueCountFrequency (%)
_2
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII803
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r218
27.1%
a113
14.1%
e111
13.8%
g111
13.8%
l111
13.8%
u109
13.6%
i9
 
1.1%
n5
 
0.6%
s4
 
0.5%
c4
 
0.5%
Other values (4)8
 
1.0%

airdate
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size1016.0 B
2020-12-30
111 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters1110
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2020-12-30
2nd row2020-12-30
3rd row2020-12-30
4th row2020-12-30
5th row2020-12-30

Common Values

ValueCountFrequency (%)
2020-12-30111
100.0%

Length

2022-05-09T21:24:09.824166image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-09T21:24:09.903960image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
2020-12-30111
100.0%

Most occurring characters

ValueCountFrequency (%)
2333
30.0%
0333
30.0%
-222
20.0%
1111
 
10.0%
3111
 
10.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number888
80.0%
Dash Punctuation222
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2333
37.5%
0333
37.5%
1111
 
12.5%
3111
 
12.5%
Dash Punctuation
ValueCountFrequency (%)
-222
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1110
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2333
30.0%
0333
30.0%
-222
20.0%
1111
 
10.0%
3111
 
10.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII1110
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2333
30.0%
0333
30.0%
-222
20.0%
1111
 
10.0%
3111
 
10.0%

airtime
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct9
Distinct (%)8.1%
Missing0
Missing (%)0.0%
Memory size1016.0 B
nan
86 
20:00
14 
12:00
 
3
18:00
 
2
00:00
 
2
Other values (4)
 
4

Length

Max length5
Median length3
Mean length3.45045045
Min length3

Characters and Unicode

Total characters383
Distinct characters11
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)3.6%

Sample

1st row12:00
2nd rownan
3rd rownan
4th row10:00
5th rownan

Common Values

ValueCountFrequency (%)
nan86
77.5%
20:0014
 
12.6%
12:003
 
2.7%
18:002
 
1.8%
00:002
 
1.8%
10:001
 
0.9%
08:301
 
0.9%
19:001
 
0.9%
21:451
 
0.9%

Length

2022-05-09T21:24:09.982308image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-09T21:24:10.092111image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
nan86
77.5%
20:0014
 
12.6%
12:003
 
2.7%
18:002
 
1.8%
00:002
 
1.8%
10:001
 
0.9%
08:301
 
0.9%
19:001
 
0.9%
21:451
 
0.9%

Most occurring characters

ValueCountFrequency (%)
n172
44.9%
a86
22.5%
067
 
17.5%
:25
 
6.5%
218
 
4.7%
18
 
2.1%
83
 
0.8%
31
 
0.3%
91
 
0.3%
41
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter258
67.4%
Decimal Number100
 
26.1%
Other Punctuation25
 
6.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
067
67.0%
218
 
18.0%
18
 
8.0%
83
 
3.0%
31
 
1.0%
91
 
1.0%
41
 
1.0%
51
 
1.0%
Lowercase Letter
ValueCountFrequency (%)
n172
66.7%
a86
33.3%
Other Punctuation
ValueCountFrequency (%)
:25
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin258
67.4%
Common125
32.6%

Most frequent character per script

Common
ValueCountFrequency (%)
067
53.6%
:25
 
20.0%
218
 
14.4%
18
 
6.4%
83
 
2.4%
31
 
0.8%
91
 
0.8%
41
 
0.8%
51
 
0.8%
Latin
ValueCountFrequency (%)
n172
66.7%
a86
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII383
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n172
44.9%
a86
22.5%
067
 
17.5%
:25
 
6.5%
218
 
4.7%
18
 
2.1%
83
 
0.8%
31
 
0.3%
91
 
0.3%
41
 
0.3%

airstamp
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct15
Distinct (%)13.5%
Missing0
Missing (%)0.0%
Memory size1016.0 B
2020-12-30T12:00:00+00:00
70 
2020-12-30T04:00:00+00:00
17 
2020-12-30T09:00:00+00:00
 
4
2020-12-30T17:00:00+00:00
 
4
2020-12-30T00:00:00+00:00
 
3
Other values (10)
13 

Length

Max length25
Median length25
Mean length25
Min length25

Characters and Unicode

Total characters2775
Distinct characters12
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique7 ?
Unique (%)6.3%

Sample

1st row2020-12-30T00:00:00+00:00
2nd row2020-12-30T00:00:00+00:00
3rd row2020-12-30T00:00:00+00:00
4th row2020-12-30T02:00:00+00:00
5th row2020-12-30T03:00:00+00:00

Common Values

ValueCountFrequency (%)
2020-12-30T12:00:00+00:0070
63.1%
2020-12-30T04:00:00+00:0017
 
15.3%
2020-12-30T09:00:00+00:004
 
3.6%
2020-12-30T17:00:00+00:004
 
3.6%
2020-12-30T00:00:00+00:003
 
2.7%
2020-12-30T03:00:00+00:002
 
1.8%
2020-12-30T11:00:00+00:002
 
1.8%
2020-12-31T01:00:00+00:002
 
1.8%
2020-12-30T02:00:00+00:001
 
0.9%
2020-12-30T10:00:00+00:001
 
0.9%
Other values (5)5
 
4.5%

Length

2022-05-09T21:24:10.186538image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2020-12-30t12:00:00+00:0070
63.1%
2020-12-30t04:00:00+00:0017
 
15.3%
2020-12-30t09:00:00+00:004
 
3.6%
2020-12-30t17:00:00+00:004
 
3.6%
2020-12-30t00:00:00+00:003
 
2.7%
2020-12-30t03:00:00+00:002
 
1.8%
2020-12-30t11:00:00+00:002
 
1.8%
2020-12-31t01:00:00+00:002
 
1.8%
2020-12-30t02:00:00+00:001
 
0.9%
2020-12-30t10:00:00+00:001
 
0.9%
Other values (5)5
 
4.5%

Most occurring characters

ValueCountFrequency (%)
01249
45.0%
2405
 
14.6%
:333
 
12.0%
-222
 
8.0%
1199
 
7.2%
3115
 
4.1%
T111
 
4.0%
+111
 
4.0%
418
 
0.6%
95
 
0.2%
Other values (2)7
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1998
72.0%
Other Punctuation333
 
12.0%
Dash Punctuation222
 
8.0%
Uppercase Letter111
 
4.0%
Math Symbol111
 
4.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
01249
62.5%
2405
 
20.3%
1199
 
10.0%
3115
 
5.8%
418
 
0.9%
95
 
0.3%
74
 
0.2%
53
 
0.2%
Other Punctuation
ValueCountFrequency (%)
:333
100.0%
Dash Punctuation
ValueCountFrequency (%)
-222
100.0%
Uppercase Letter
ValueCountFrequency (%)
T111
100.0%
Math Symbol
ValueCountFrequency (%)
+111
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common2664
96.0%
Latin111
 
4.0%

Most frequent character per script

Common
ValueCountFrequency (%)
01249
46.9%
2405
 
15.2%
:333
 
12.5%
-222
 
8.3%
1199
 
7.5%
3115
 
4.3%
+111
 
4.2%
418
 
0.7%
95
 
0.2%
74
 
0.2%
Latin
ValueCountFrequency (%)
T111
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII2775
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
01249
45.0%
2405
 
14.6%
:333
 
12.0%
-222
 
8.0%
1199
 
7.2%
3115
 
4.1%
T111
 
4.0%
+111
 
4.0%
418
 
0.6%
95
 
0.2%
Other values (2)7
 
0.3%

runtime
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct41
Distinct (%)39.8%
Missing8
Missing (%)7.2%
Infinite0
Infinite (%)0.0%
Mean40.09708738
Minimum4
Maximum208
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1016.0 B
2022-05-09T21:24:10.280988image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile12
Q124
median36
Q345
95-th percentile92.7
Maximum208
Range204
Interquartile range (IQR)21

Descriptive statistics

Standard deviation28.80342932
Coefficient of variation (CV)0.7183421839
Kurtosis12.06077032
Mean40.09708738
Median Absolute Deviation (MAD)11
Skewness2.841838941
Sum4130
Variance829.6375405
MonotonicityNot monotonic
2022-05-09T21:24:10.376365image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=41)
ValueCountFrequency (%)
4521
18.9%
309
 
8.1%
205
 
4.5%
125
 
4.5%
355
 
4.5%
255
 
4.5%
244
 
3.6%
364
 
3.6%
193
 
2.7%
403
 
2.7%
Other values (31)39
35.1%
(Missing)8
 
7.2%
ValueCountFrequency (%)
41
 
0.9%
51
 
0.9%
61
 
0.9%
101
 
0.9%
125
4.5%
131
 
0.9%
153
2.7%
171
 
0.9%
181
 
0.9%
193
2.7%
ValueCountFrequency (%)
2081
 
0.9%
1271
 
0.9%
1203
2.7%
931
 
0.9%
901
 
0.9%
661
 
0.9%
641
 
0.9%
621
 
0.9%
611
 
0.9%
602
1.8%

summary
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct41
Distinct (%)36.9%
Missing0
Missing (%)0.0%
Memory size1016.0 B
nan
71 
<p>This week, Ty Franck and Wes Chatham welcome one of the longest standing crew members and one of the newest to The Expanse Aftershow. Watch Jeff Woolnough, director of episode 505, and Jasai Chase Owens, who plays Filip, join the chat to discuss the latest episode of The Expanse Season 5.</p>
 
1
<p>Game day appetizers morph into meals that score big points with the judges. The contestants must reimagine smoky barbecue takeout as a gourmet feast.</p><p><br /> </p>
 
1
<p>A festive meal means lots of leftovers. So how about glazed ham, apple pie and green bean casserole… on a sandwich? Later, Greek food gets a new life.</p><p><br /> </p>
 
1
<p>The cooks serve up a spectacular breakfast using fancy date-night leftovers from the fridge, then wow the judges with fresh takes on Italian takeout.</p><p><br /> </p>
 
1
Other values (36)
36 

Length

Max length375
Median length3
Mean length66.87387387
Min length3

Characters and Unicode

Total characters7423
Distinct characters70
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique40 ?
Unique (%)36.0%

Sample

1st rownan
2nd row<p>A unique release of The Shocking Howe. Actors and writers of the "Comfort Zone" series will tell you how the project was created. Of course, everything is in the style of the Shocking Howe.</p>
3rd rownan
4th rownan
5th rownan

Common Values

ValueCountFrequency (%)
nan71
64.0%
<p>This week, Ty Franck and Wes Chatham welcome one of the longest standing crew members and one of the newest to The Expanse Aftershow. Watch Jeff Woolnough, director of episode 505, and Jasai Chase Owens, who plays Filip, join the chat to discuss the latest episode of The Expanse Season 5.</p>1
 
0.9%
<p>Game day appetizers morph into meals that score big points with the judges. The contestants must reimagine smoky barbecue takeout as a gourmet feast.</p><p><br /> </p>1
 
0.9%
<p>A festive meal means lots of leftovers. So how about glazed ham, apple pie and green bean casserole… on a sandwich? Later, Greek food gets a new life.</p><p><br /> </p>1
 
0.9%
<p>The cooks serve up a spectacular breakfast using fancy date-night leftovers from the fridge, then wow the judges with fresh takes on Italian takeout.</p><p><br /> </p>1
 
0.9%
<p>Bacon-wrapped brats and blueberry cobbler get a second look as late-night snacks, and duck confit and cassoulet evolve into entirely different dishes.</p><p><br /> </p>1
 
0.9%
<p>Potluck castoffs return with a vengeance as sinful desserts before the challengers tackle chilled Chinese takeout, including egg rolls and chow mein.</p><p><br /> </p>1
 
0.9%
<p>The cooks use food from a children's party to make cocktails and an indulgent brunch. Mexican dishes are remade into gnocchi, dumplings and more.</p><p><br /> </p>1
 
0.9%
<p>After creating a "flavor bomb" from notoriously bland foods like rice and toast, the competitors take on a new challenge: transforming Thai takeout.</p>1
 
0.9%
<p>Cheap heroin begins to flood Italy. With the government unable to deal with the damage, Vincenzo Muccioli opens a commune to treat addicted youth.</p><p><br /> </p>1
 
0.9%
Other values (31)31
27.9%

Length

2022-05-09T21:24:10.525692image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
the76
 
6.2%
nan71
 
5.8%
to38
 
3.1%
and32
 
2.6%
a31
 
2.5%
22
 
1.8%
p21
 
1.7%
of20
 
1.6%
in15
 
1.2%
with12
 
1.0%
Other values (656)887
72.4%

Most occurring characters

ValueCountFrequency (%)
1093
14.7%
e676
 
9.1%
a507
 
6.8%
n486
 
6.5%
t448
 
6.0%
o415
 
5.6%
s398
 
5.4%
i334
 
4.5%
r334
 
4.5%
h264
 
3.6%
Other values (60)2468
33.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter5535
74.6%
Space Separator1114
 
15.0%
Math Symbol286
 
3.9%
Other Punctuation240
 
3.2%
Uppercase Letter213
 
2.9%
Decimal Number20
 
0.3%
Dash Punctuation15
 
0.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e676
12.2%
a507
 
9.2%
n486
 
8.8%
t448
 
8.1%
o415
 
7.5%
s398
 
7.2%
i334
 
6.0%
r334
 
6.0%
h264
 
4.8%
p235
 
4.2%
Other values (16)1438
26.0%
Uppercase Letter
ValueCountFrequency (%)
A39
18.3%
T23
 
10.8%
M17
 
8.0%
C15
 
7.0%
S14
 
6.6%
I11
 
5.2%
H10
 
4.7%
L9
 
4.2%
W9
 
4.2%
D8
 
3.8%
Other values (13)58
27.2%
Other Punctuation
ValueCountFrequency (%)
/82
34.2%
.78
32.5%
,44
18.3%
'19
 
7.9%
"8
 
3.3%
!4
 
1.7%
?2
 
0.8%
;1
 
0.4%
1
 
0.4%
:1
 
0.4%
Decimal Number
ValueCountFrequency (%)
27
35.0%
07
35.0%
53
15.0%
12
 
10.0%
31
 
5.0%
Space Separator
ValueCountFrequency (%)
1093
98.1%
 21
 
1.9%
Math Symbol
ValueCountFrequency (%)
<143
50.0%
>143
50.0%
Dash Punctuation
ValueCountFrequency (%)
-12
80.0%
3
 
20.0%

Most occurring scripts

ValueCountFrequency (%)
Latin5748
77.4%
Common1675
 
22.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
e676
11.8%
a507
 
8.8%
n486
 
8.5%
t448
 
7.8%
o415
 
7.2%
s398
 
6.9%
i334
 
5.8%
r334
 
5.8%
h264
 
4.6%
p235
 
4.1%
Other values (39)1651
28.7%
Common
ValueCountFrequency (%)
1093
65.3%
<143
 
8.5%
>143
 
8.5%
/82
 
4.9%
.78
 
4.7%
,44
 
2.6%
 21
 
1.3%
'19
 
1.1%
-12
 
0.7%
"8
 
0.5%
Other values (11)32
 
1.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII7398
99.7%
None21
 
0.3%
Punctuation4
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1093
14.8%
e676
 
9.1%
a507
 
6.9%
n486
 
6.6%
t448
 
6.1%
o415
 
5.6%
s398
 
5.4%
i334
 
4.5%
r334
 
4.5%
h264
 
3.6%
Other values (57)2443
33.0%
None
ValueCountFrequency (%)
 21
100.0%
Punctuation
ValueCountFrequency (%)
3
75.0%
1
 
25.0%

_embedded_show_id
Real number (ℝ≥0)

HIGH CORRELATION

Distinct70
Distinct (%)63.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean48392.76577
Minimum1825
Maximum61755
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1016.0 B
2022-05-09T21:24:10.635295image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1825
5-th percentile17110.5
Q144083
median52471
Q353014.5
95-th percentile61556
Maximum61755
Range59930
Interquartile range (IQR)8931.5

Descriptive statistics

Standard deviation12251.35313
Coefficient of variation (CV)0.2531649708
Kurtosis5.732458235
Mean48392.76577
Median Absolute Deviation (MAD)2841
Skewness-2.295693991
Sum5371597
Variance150095653.5
MonotonicityNot monotonic
2022-05-09T21:24:10.748528image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
526158
 
7.2%
615566
 
5.4%
423876
 
5.4%
438906
 
5.4%
527085
 
4.5%
521043
 
2.7%
152502
 
1.8%
525242
 
1.8%
472262
 
1.8%
529362
 
1.8%
Other values (60)69
62.2%
ValueCountFrequency (%)
18251
0.9%
22661
0.9%
25041
0.9%
64411
0.9%
152502
1.8%
189711
0.9%
283461
0.9%
306061
0.9%
339441
0.9%
340601
0.9%
ValueCountFrequency (%)
617551
 
0.9%
615566
5.4%
612472
 
1.8%
605081
 
0.9%
601611
 
0.9%
586892
 
1.8%
586451
 
0.9%
584261
 
0.9%
574781
 
0.9%
567831
 
0.9%

_embedded_show_url
Categorical

HIGH CARDINALITY
HIGH CORRELATION
HIGH CORRELATION

Distinct70
Distinct (%)63.1%
Missing0
Missing (%)0.0%
Memory size1016.0 B
https://www.tvmaze.com/shows/52615/best-leftovers-ever
 
8
https://www.tvmaze.com/shows/61556/unsolved-cases-of-kung-fu-portrait-of-beauty
 
6
https://www.tvmaze.com/shows/42387/transformers-war-for-cybertron-trilogy
 
6
https://www.tvmaze.com/shows/43890/equinox
 
6
https://www.tvmaze.com/shows/52708/sanpa-luci-e-tenebre-di-san-patrignano
 
5
Other values (65)
80 

Length

Max length79
Median length59
Mean length54.43243243
Min length39

Characters and Unicode

Total characters6042
Distinct characters40
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique51 ?
Unique (%)45.9%

Sample

1st rowhttps://www.tvmaze.com/shows/49630/kontakty
2nd rowhttps://www.tvmaze.com/shows/51510/zona-komforta
3rd rowhttps://www.tvmaze.com/shows/52499/passaziry
4th rowhttps://www.tvmaze.com/shows/49652/yi-nian-yong-heng
5th rowhttps://www.tvmaze.com/shows/44276/7-days-of-romance

Common Values

ValueCountFrequency (%)
https://www.tvmaze.com/shows/52615/best-leftovers-ever8
 
7.2%
https://www.tvmaze.com/shows/61556/unsolved-cases-of-kung-fu-portrait-of-beauty6
 
5.4%
https://www.tvmaze.com/shows/42387/transformers-war-for-cybertron-trilogy6
 
5.4%
https://www.tvmaze.com/shows/43890/equinox6
 
5.4%
https://www.tvmaze.com/shows/52708/sanpa-luci-e-tenebre-di-san-patrignano5
 
4.5%
https://www.tvmaze.com/shows/52104/twisted-fate-of-love3
 
2.7%
https://www.tvmaze.com/shows/15250/the-young-turks2
 
1.8%
https://www.tvmaze.com/shows/52524/forever-love2
 
1.8%
https://www.tvmaze.com/shows/47226/arashis-diary-voyage2
 
1.8%
https://www.tvmaze.com/shows/52936/my-best-friends-story2
 
1.8%
Other values (60)69
62.2%

Length

2022-05-09T21:24:10.882417image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
https://www.tvmaze.com/shows/52615/best-leftovers-ever8
 
7.2%
https://www.tvmaze.com/shows/42387/transformers-war-for-cybertron-trilogy6
 
5.4%
https://www.tvmaze.com/shows/43890/equinox6
 
5.4%
https://www.tvmaze.com/shows/61556/unsolved-cases-of-kung-fu-portrait-of-beauty6
 
5.4%
https://www.tvmaze.com/shows/52708/sanpa-luci-e-tenebre-di-san-patrignano5
 
4.5%
https://www.tvmaze.com/shows/52104/twisted-fate-of-love3
 
2.7%
https://www.tvmaze.com/shows/58689/my-supernatural-power2
 
1.8%
https://www.tvmaze.com/shows/52421/you-complete-me2
 
1.8%
https://www.tvmaze.com/shows/52400/dream-detective2
 
1.8%
https://www.tvmaze.com/shows/51749/shi-yi-changan-mingyue-ji-shi-you2
 
1.8%
Other values (60)69
62.2%

Most occurring characters

ValueCountFrequency (%)
/555
 
9.2%
t471
 
7.8%
w466
 
7.7%
s458
 
7.6%
o372
 
6.2%
e341
 
5.6%
-269
 
4.5%
h264
 
4.4%
m263
 
4.4%
a254
 
4.2%
Other values (30)2329
38.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter4320
71.5%
Other Punctuation888
 
14.7%
Decimal Number565
 
9.4%
Dash Punctuation269
 
4.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t471
10.9%
w466
10.8%
s458
10.6%
o372
 
8.6%
e341
 
7.9%
h264
 
6.1%
m263
 
6.1%
a254
 
5.9%
c153
 
3.5%
r152
 
3.5%
Other values (16)1126
26.1%
Decimal Number
ValueCountFrequency (%)
5105
18.6%
276
13.5%
468
12.0%
657
10.1%
150
8.8%
047
8.3%
846
8.1%
742
 
7.4%
340
 
7.1%
934
 
6.0%
Other Punctuation
ValueCountFrequency (%)
/555
62.5%
.222
 
25.0%
:111
 
12.5%
Dash Punctuation
ValueCountFrequency (%)
-269
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin4320
71.5%
Common1722
 
28.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
t471
10.9%
w466
10.8%
s458
10.6%
o372
 
8.6%
e341
 
7.9%
h264
 
6.1%
m263
 
6.1%
a254
 
5.9%
c153
 
3.5%
r152
 
3.5%
Other values (16)1126
26.1%
Common
ValueCountFrequency (%)
/555
32.2%
-269
15.6%
.222
 
12.9%
:111
 
6.4%
5105
 
6.1%
276
 
4.4%
468
 
3.9%
657
 
3.3%
150
 
2.9%
047
 
2.7%
Other values (4)162
 
9.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII6042
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
/555
 
9.2%
t471
 
7.8%
w466
 
7.7%
s458
 
7.6%
o372
 
6.2%
e341
 
5.6%
-269
 
4.5%
h264
 
4.4%
m263
 
4.4%
a254
 
4.2%
Other values (30)2329
38.5%

_embedded_show_name
Categorical

HIGH CARDINALITY
HIGH CORRELATION
HIGH CORRELATION

Distinct70
Distinct (%)63.1%
Missing0
Missing (%)0.0%
Memory size1016.0 B
Best Leftovers Ever!
 
8
Unsolved Cases of Kung Fu: Portrait of Beauty
 
6
Transformers: War for Cybertron Trilogy
 
6
Equinox
 
6
SanPa: Luci e tenebre di San Patrignano
 
5
Other values (65)
80 

Length

Max length45
Median length25
Mean length19.87387387
Min length5

Characters and Unicode

Total characters2206
Distinct characters99
Distinct categories5 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique51 ?
Unique (%)45.9%

Sample

1st rowКонтакты
2nd rowЗона комфорта
3rd rowПассажиры
4th rowYi Nian Yong Heng
5th row7 Days of Romance

Common Values

ValueCountFrequency (%)
Best Leftovers Ever!8
 
7.2%
Unsolved Cases of Kung Fu: Portrait of Beauty6
 
5.4%
Transformers: War for Cybertron Trilogy6
 
5.4%
Equinox6
 
5.4%
SanPa: Luci e tenebre di San Patrignano5
 
4.5%
Twisted Fate of Love3
 
2.7%
The Young Turks2
 
1.8%
Forever Love2
 
1.8%
Arashi's Diary: Voyage2
 
1.8%
My Best Friend's Story2
 
1.8%
Other values (60)69
62.2%

Length

2022-05-09T21:24:11.085135image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
of20
 
5.3%
best10
 
2.6%
the10
 
2.6%
leftovers8
 
2.1%
ever8
 
2.1%
cases6
 
1.6%
for6
 
1.6%
love6
 
1.6%
my6
 
1.6%
equinox6
 
1.6%
Other values (170)294
77.4%

Most occurring characters

ValueCountFrequency (%)
269
 
12.2%
e203
 
9.2%
r139
 
6.3%
o135
 
6.1%
n115
 
5.2%
a113
 
5.1%
i102
 
4.6%
t96
 
4.4%
s89
 
4.0%
u64
 
2.9%
Other values (89)881
39.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter1548
70.2%
Uppercase Letter332
 
15.0%
Space Separator269
 
12.2%
Other Punctuation43
 
1.9%
Decimal Number14
 
0.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e203
13.1%
r139
 
9.0%
o135
 
8.7%
n115
 
7.4%
a113
 
7.3%
i102
 
6.6%
t96
 
6.2%
s89
 
5.7%
u64
 
4.1%
y44
 
2.8%
Other values (42)448
28.9%
Uppercase Letter
ValueCountFrequency (%)
T32
 
9.6%
L30
 
9.0%
S29
 
8.7%
B23
 
6.9%
C23
 
6.9%
P22
 
6.6%
E19
 
5.7%
F18
 
5.4%
D15
 
4.5%
A15
 
4.5%
Other values (25)106
31.9%
Other Punctuation
ValueCountFrequency (%)
:25
58.1%
!8
 
18.6%
'7
 
16.3%
?1
 
2.3%
.1
 
2.3%
,1
 
2.3%
Decimal Number
ValueCountFrequency (%)
25
35.7%
04
28.6%
13
21.4%
51
 
7.1%
71
 
7.1%
Space Separator
ValueCountFrequency (%)
269
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin1752
79.4%
Common326
 
14.8%
Cyrillic128
 
5.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
e203
 
11.6%
r139
 
7.9%
o135
 
7.7%
n115
 
6.6%
a113
 
6.4%
i102
 
5.8%
t96
 
5.5%
s89
 
5.1%
u64
 
3.7%
y44
 
2.5%
Other values (41)652
37.2%
Cyrillic
ValueCountFrequency (%)
о14
 
10.9%
а12
 
9.4%
и11
 
8.6%
т9
 
7.0%
р8
 
6.2%
н8
 
6.2%
к7
 
5.5%
е6
 
4.7%
с6
 
4.7%
з4
 
3.1%
Other values (26)43
33.6%
Common
ValueCountFrequency (%)
269
82.5%
:25
 
7.7%
!8
 
2.5%
'7
 
2.1%
25
 
1.5%
04
 
1.2%
13
 
0.9%
51
 
0.3%
?1
 
0.3%
.1
 
0.3%
Other values (2)2
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII2077
94.2%
Cyrillic128
 
5.8%
None1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
269
 
13.0%
e203
 
9.8%
r139
 
6.7%
o135
 
6.5%
n115
 
5.5%
a113
 
5.4%
i102
 
4.9%
t96
 
4.6%
s89
 
4.3%
u64
 
3.1%
Other values (52)752
36.2%
Cyrillic
ValueCountFrequency (%)
о14
 
10.9%
а12
 
9.4%
и11
 
8.6%
т9
 
7.0%
р8
 
6.2%
н8
 
6.2%
к7
 
5.5%
е6
 
4.7%
с6
 
4.7%
з4
 
3.1%
Other values (26)43
33.6%
None
ValueCountFrequency (%)
ė1
100.0%

_embedded_show_type
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct9
Distinct (%)8.1%
Missing0
Missing (%)0.0%
Memory size1016.0 B
Scripted
54 
Animation
15 
Reality
11 
Documentary
10 
Talk Show
Other values (4)
12 

Length

Max length11
Median length9
Mean length8.252252252
Min length4

Characters and Unicode

Total characters916
Distinct characters27
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowGame Show
2nd rowScripted
3rd rowScripted
4th rowAnimation
5th rowScripted

Common Values

ValueCountFrequency (%)
Scripted54
48.6%
Animation15
 
13.5%
Reality11
 
9.9%
Documentary10
 
9.0%
Talk Show9
 
8.1%
Variety4
 
3.6%
Game Show3
 
2.7%
Sports3
 
2.7%
News2
 
1.8%

Length

2022-05-09T21:24:11.194756image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-09T21:24:11.304760image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
scripted54
43.9%
animation15
 
12.2%
show12
 
9.8%
reality11
 
8.9%
documentary10
 
8.1%
talk9
 
7.3%
variety4
 
3.3%
game3
 
2.4%
sports3
 
2.4%
news2
 
1.6%

Most occurring characters

ValueCountFrequency (%)
i99
10.8%
t97
10.6%
e84
 
9.2%
r71
 
7.8%
S69
 
7.5%
c64
 
7.0%
p57
 
6.2%
d54
 
5.9%
a52
 
5.7%
o40
 
4.4%
Other values (17)229
25.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter781
85.3%
Uppercase Letter123
 
13.4%
Space Separator12
 
1.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i99
12.7%
t97
12.4%
e84
10.8%
r71
9.1%
c64
8.2%
p57
7.3%
d54
6.9%
a52
6.7%
o40
 
5.1%
n40
 
5.1%
Other values (8)123
15.7%
Uppercase Letter
ValueCountFrequency (%)
S69
56.1%
A15
 
12.2%
R11
 
8.9%
D10
 
8.1%
T9
 
7.3%
V4
 
3.3%
G3
 
2.4%
N2
 
1.6%
Space Separator
ValueCountFrequency (%)
12
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin904
98.7%
Common12
 
1.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
i99
11.0%
t97
10.7%
e84
 
9.3%
r71
 
7.9%
S69
 
7.6%
c64
 
7.1%
p57
 
6.3%
d54
 
6.0%
a52
 
5.8%
o40
 
4.4%
Other values (16)217
24.0%
Common
ValueCountFrequency (%)
12
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII916
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
i99
10.8%
t97
10.6%
e84
 
9.2%
r71
 
7.8%
S69
 
7.5%
c64
 
7.0%
p57
 
6.2%
d54
 
5.9%
a52
 
5.7%
o40
 
4.4%
Other values (17)229
25.0%

_embedded_show_language
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct20
Distinct (%)18.0%
Missing0
Missing (%)0.0%
Memory size1016.0 B
Chinese
33 
English
31 
Russian
Danish
Korean
Other values (15)
28 

Length

Max length10
Median length7
Mean length6.945945946
Min length3

Characters and Unicode

Total characters771
Distinct characters34
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique8 ?
Unique (%)7.2%

Sample

1st rowRussian
2nd rowRussian
3rd rowRussian
4th rowChinese
5th rowKorean

Common Values

ValueCountFrequency (%)
Chinese33
29.7%
English31
27.9%
Russian8
 
7.2%
Danish6
 
5.4%
Korean5
 
4.5%
Italian5
 
4.5%
Arabic3
 
2.7%
Norwegian3
 
2.7%
Japanese3
 
2.7%
Ukrainian2
 
1.8%
Other values (10)12
 
10.8%

Length

2022-05-09T21:24:11.414405image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
chinese33
29.7%
english31
27.9%
russian8
 
7.2%
danish6
 
5.4%
korean5
 
4.5%
italian5
 
4.5%
arabic3
 
2.7%
norwegian3
 
2.7%
japanese3
 
2.7%
turkish2
 
1.8%
Other values (10)12
 
10.8%

Most occurring characters

ValueCountFrequency (%)
n106
13.7%
i102
13.2%
s92
11.9%
e85
11.0%
h76
9.9%
a57
7.4%
g37
 
4.8%
l37
 
4.8%
C33
 
4.3%
E31
 
4.0%
Other values (24)115
14.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter661
85.7%
Uppercase Letter110
 
14.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n106
16.0%
i102
15.4%
s92
13.9%
e85
12.9%
h76
11.5%
a57
8.6%
g37
 
5.6%
l37
 
5.6%
r17
 
2.6%
u13
 
2.0%
Other values (9)39
 
5.9%
Uppercase Letter
ValueCountFrequency (%)
C33
30.0%
E31
28.2%
R8
 
7.3%
D7
 
6.4%
K5
 
4.5%
I5
 
4.5%
T5
 
4.5%
J3
 
2.7%
N3
 
2.7%
A3
 
2.7%
Other values (5)7
 
6.4%

Most occurring scripts

ValueCountFrequency (%)
Latin771
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
n106
13.7%
i102
13.2%
s92
11.9%
e85
11.0%
h76
9.9%
a57
7.4%
g37
 
4.8%
l37
 
4.8%
C33
 
4.3%
E31
 
4.0%
Other values (24)115
14.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII771
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n106
13.7%
i102
13.2%
s92
11.9%
e85
11.0%
h76
9.9%
a57
7.4%
g37
 
4.8%
l37
 
4.8%
C33
 
4.3%
E31
 
4.0%
Other values (24)115
14.9%

_embedded_show_genres
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct34
Distinct (%)30.6%
Missing0
Missing (%)0.0%
Memory size1016.0 B
[]
21 
['Drama', 'Romance']
13 
['Food']
['Mystery']
['Comedy']
Other values (29)
57 

Length

Max length42
Median length36
Mean length17.83783784
Min length2

Characters and Unicode

Total characters1980
Distinct characters33
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique16 ?
Unique (%)14.4%

Sample

1st row[]
2nd row['Comedy']
3rd row['Drama', 'Supernatural']
4th row['Comedy', 'Action', 'Anime', 'Fantasy']
5th row['Drama', 'Romance']

Common Values

ValueCountFrequency (%)
[]21
18.9%
['Drama', 'Romance']13
 
11.7%
['Food']8
 
7.2%
['Mystery']6
 
5.4%
['Comedy']6
 
5.4%
['Action', 'Adventure', 'Science-Fiction']6
 
5.4%
['Drama', 'Thriller', 'Supernatural']6
 
5.4%
['Crime']5
 
4.5%
['Drama', 'Romance', 'History']4
 
3.6%
['Drama', 'Comedy', 'Romance']3
 
2.7%
Other values (24)33
29.7%

Length

2022-05-09T21:24:11.539743image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
drama39
19.4%
romance26
12.9%
21
10.4%
comedy18
9.0%
mystery11
 
5.5%
action11
 
5.5%
food9
 
4.5%
crime8
 
4.0%
supernatural8
 
4.0%
thriller8
 
4.0%
Other values (9)42
20.9%

Most occurring characters

ValueCountFrequency (%)
'360
18.2%
a136
 
6.9%
e120
 
6.1%
[111
 
5.6%
]111
 
5.6%
r107
 
5.4%
m98
 
4.9%
,90
 
4.5%
90
 
4.5%
o86
 
4.3%
Other values (23)671
33.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter1022
51.6%
Other Punctuation450
22.7%
Uppercase Letter188
 
9.5%
Open Punctuation111
 
5.6%
Close Punctuation111
 
5.6%
Space Separator90
 
4.5%
Dash Punctuation8
 
0.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a136
13.3%
e120
11.7%
r107
10.5%
m98
9.6%
o86
8.4%
n85
8.3%
i69
6.8%
c64
6.3%
t58
 
5.7%
y53
 
5.2%
Other values (7)146
14.3%
Uppercase Letter
ValueCountFrequency (%)
D39
20.7%
C29
15.4%
R26
13.8%
F25
13.3%
A25
13.3%
S16
8.5%
M14
 
7.4%
T8
 
4.3%
H5
 
2.7%
W1
 
0.5%
Other Punctuation
ValueCountFrequency (%)
'360
80.0%
,90
 
20.0%
Open Punctuation
ValueCountFrequency (%)
[111
100.0%
Close Punctuation
ValueCountFrequency (%)
]111
100.0%
Space Separator
ValueCountFrequency (%)
90
100.0%
Dash Punctuation
ValueCountFrequency (%)
-8
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin1210
61.1%
Common770
38.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
a136
11.2%
e120
 
9.9%
r107
 
8.8%
m98
 
8.1%
o86
 
7.1%
n85
 
7.0%
i69
 
5.7%
c64
 
5.3%
t58
 
4.8%
y53
 
4.4%
Other values (17)334
27.6%
Common
ValueCountFrequency (%)
'360
46.8%
[111
 
14.4%
]111
 
14.4%
,90
 
11.7%
90
 
11.7%
-8
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII1980
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
'360
18.2%
a136
 
6.9%
e120
 
6.1%
[111
 
5.6%
]111
 
5.6%
r107
 
5.4%
m98
 
4.9%
,90
 
4.5%
90
 
4.5%
o86
 
4.3%
Other values (23)671
33.9%

_embedded_show_status
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Memory size1016.0 B
Ended
49 
Running
42 
To Be Determined
20 

Length

Max length16
Median length7
Mean length7.738738739
Min length5

Characters and Unicode

Total characters859
Distinct characters16
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowRunning
2nd rowEnded
3rd rowRunning
4th rowRunning
5th rowEnded

Common Values

ValueCountFrequency (%)
Ended49
44.1%
Running42
37.8%
To Be Determined20
18.0%

Length

2022-05-09T21:24:11.666248image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-09T21:24:11.767613image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
ended49
32.5%
running42
27.8%
to20
13.2%
be20
13.2%
determined20
13.2%

Most occurring characters

ValueCountFrequency (%)
n195
22.7%
e129
15.0%
d118
13.7%
i62
 
7.2%
E49
 
5.7%
R42
 
4.9%
u42
 
4.9%
g42
 
4.9%
40
 
4.7%
T20
 
2.3%
Other values (6)120
14.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter668
77.8%
Uppercase Letter151
 
17.6%
Space Separator40
 
4.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n195
29.2%
e129
19.3%
d118
17.7%
i62
 
9.3%
u42
 
6.3%
g42
 
6.3%
o20
 
3.0%
t20
 
3.0%
r20
 
3.0%
m20
 
3.0%
Uppercase Letter
ValueCountFrequency (%)
E49
32.5%
R42
27.8%
T20
13.2%
B20
13.2%
D20
13.2%
Space Separator
ValueCountFrequency (%)
40
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin819
95.3%
Common40
 
4.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
n195
23.8%
e129
15.8%
d118
14.4%
i62
 
7.6%
E49
 
6.0%
R42
 
5.1%
u42
 
5.1%
g42
 
5.1%
T20
 
2.4%
o20
 
2.4%
Other values (5)100
12.2%
Common
ValueCountFrequency (%)
40
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII859
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n195
22.7%
e129
15.0%
d118
13.7%
i62
 
7.2%
E49
 
5.7%
R42
 
4.9%
u42
 
4.9%
g42
 
4.9%
40
 
4.7%
T20
 
2.3%
Other values (6)120
14.0%

_embedded_show_runtime
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct18
Distinct (%)28.6%
Missing48
Missing (%)43.2%
Infinite0
Infinite (%)0.0%
Mean41.22222222
Minimum4
Maximum120
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1016.0 B
2022-05-09T21:24:11.845738image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile12.3
Q124
median38
Q345
95-th percentile117
Maximum120
Range116
Interquartile range (IQR)21

Descriptive statistics

Standard deviation26.50529128
Coefficient of variation (CV)0.6429855028
Kurtosis3.1201341
Mean41.22222222
Median Absolute Deviation (MAD)8
Skewness1.725275084
Sum2597
Variance702.5304659
MonotonicityNot monotonic
2022-05-09T21:24:11.939802image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
4519
 
17.1%
3010
 
9.0%
208
 
7.2%
1204
 
3.6%
603
 
2.7%
253
 
2.7%
382
 
1.8%
402
 
1.8%
902
 
1.8%
122
 
1.8%
Other values (8)8
 
7.2%
(Missing)48
43.2%
ValueCountFrequency (%)
41
 
0.9%
51
 
0.9%
122
 
1.8%
151
 
0.9%
191
 
0.9%
208
7.2%
221
 
0.9%
231
 
0.9%
253
 
2.7%
3010
9.0%
ValueCountFrequency (%)
1204
 
3.6%
902
 
1.8%
661
 
0.9%
603
 
2.7%
4519
17.1%
402
 
1.8%
382
 
1.8%
331
 
0.9%
3010
9.0%
253
 
2.7%

_embedded_show_averageRuntime
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct34
Distinct (%)32.1%
Missing5
Missing (%)4.5%
Infinite0
Infinite (%)0.0%
Mean38.05660377
Minimum4
Maximum120
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1016.0 B
2022-05-09T21:24:12.042442image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile12
Q125
median36
Q345
95-th percentile75.75
Maximum120
Range116
Interquartile range (IQR)20

Descriptive statistics

Standard deviation22.48081942
Coefficient of variation (CV)0.5907205896
Kurtosis4.224712472
Mean38.05660377
Median Absolute Deviation (MAD)10
Skewness1.697654214
Sum4034
Variance505.3872417
MonotonicityNot monotonic
2022-05-09T21:24:12.147949image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
4518
16.2%
3011
 
9.9%
2510
 
9.0%
369
 
8.1%
607
 
6.3%
206
 
5.4%
466
 
5.4%
125
 
4.5%
1203
 
2.7%
423
 
2.7%
Other values (24)28
25.2%
(Missing)5
 
4.5%
ValueCountFrequency (%)
41
 
0.9%
52
 
1.8%
91
 
0.9%
101
 
0.9%
125
4.5%
131
 
0.9%
151
 
0.9%
161
 
0.9%
171
 
0.9%
181
 
0.9%
ValueCountFrequency (%)
1203
2.7%
1101
 
0.9%
901
 
0.9%
761
 
0.9%
751
 
0.9%
607
6.3%
581
 
0.9%
571
 
0.9%
551
 
0.9%
466
5.4%

_embedded_show_premiered
Categorical

HIGH CARDINALITY
HIGH CORRELATION
HIGH CORRELATION

Distinct52
Distinct (%)46.8%
Missing0
Missing (%)0.0%
Memory size1016.0 B
2020-12-30
35 
2020-07-30
 
6
2020-12-16
 
4
2020-11-23
 
3
2020-12-24
 
3
Other values (47)
60 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters1110
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique37 ?
Unique (%)33.3%

Sample

1st row2019-04-03
2nd row2020-10-22
3rd row2020-12-24
4th row2020-08-12
5th row2019-10-08

Common Values

ValueCountFrequency (%)
2020-12-3035
31.5%
2020-07-306
 
5.4%
2020-12-164
 
3.6%
2020-11-233
 
2.7%
2020-12-243
 
2.7%
2020-12-143
 
2.7%
2020-12-083
 
2.7%
2020-11-183
 
2.7%
2013-12-242
 
1.8%
2020-12-022
 
1.8%
Other values (42)47
42.3%

Length

2022-05-09T21:24:12.235545image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2020-12-3035
31.5%
2020-07-306
 
5.4%
2020-12-164
 
3.6%
2020-11-233
 
2.7%
2020-12-243
 
2.7%
2020-12-143
 
2.7%
2020-12-083
 
2.7%
2020-11-183
 
2.7%
2019-12-312
 
1.8%
2019-01-172
 
1.8%
Other values (42)47
42.3%

Most occurring characters

ValueCountFrequency (%)
2290
26.1%
0284
25.6%
-222
20.0%
1160
14.4%
358
 
5.2%
925
 
2.3%
820
 
1.8%
718
 
1.6%
416
 
1.4%
611
 
1.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number888
80.0%
Dash Punctuation222
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2290
32.7%
0284
32.0%
1160
18.0%
358
 
6.5%
925
 
2.8%
820
 
2.3%
718
 
2.0%
416
 
1.8%
611
 
1.2%
56
 
0.7%
Dash Punctuation
ValueCountFrequency (%)
-222
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1110
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2290
26.1%
0284
25.6%
-222
20.0%
1160
14.4%
358
 
5.2%
925
 
2.3%
820
 
1.8%
718
 
1.6%
416
 
1.4%
611
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII1110
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2290
26.1%
0284
25.6%
-222
20.0%
1160
14.4%
358
 
5.2%
925
 
2.3%
820
 
1.8%
718
 
1.6%
416
 
1.4%
611
 
1.0%

_embedded_show_ended
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct18
Distinct (%)16.2%
Missing0
Missing (%)0.0%
Memory size1016.0 B
nan
62 
2020-12-30
12 
2021-01-07
2021-07-29
 
6
2021-01-05
 
4
Other values (13)
19 

Length

Max length10
Median length3
Mean length6.09009009
Min length3

Characters and Unicode

Total characters676
Distinct characters13
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique7 ?
Unique (%)6.3%

Sample

1st rownan
2nd row2022-03-02
3rd rownan
4th rownan
5th row2021-01-20

Common Values

ValueCountFrequency (%)
nan62
55.9%
2020-12-3012
 
10.8%
2021-01-078
 
7.2%
2021-07-296
 
5.4%
2021-01-054
 
3.6%
2021-02-282
 
1.8%
2021-02-032
 
1.8%
2022-01-012
 
1.8%
2021-01-272
 
1.8%
2021-01-202
 
1.8%
Other values (8)9
 
8.1%

Length

2022-05-09T21:24:12.329983image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
nan62
55.9%
2020-12-3012
 
10.8%
2021-01-078
 
7.2%
2021-07-296
 
5.4%
2021-01-054
 
3.6%
2021-01-272
 
1.8%
2021-01-142
 
1.8%
2021-01-202
 
1.8%
2022-01-012
 
1.8%
2021-02-032
 
1.8%
Other values (8)9
 
8.1%

Most occurring characters

ValueCountFrequency (%)
2134
19.8%
0131
19.4%
n124
18.3%
-98
14.5%
175
11.1%
a62
9.2%
320
 
3.0%
716
 
2.4%
96
 
0.9%
54
 
0.6%
Other values (3)6
 
0.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number392
58.0%
Lowercase Letter186
27.5%
Dash Punctuation98
 
14.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2134
34.2%
0131
33.4%
175
19.1%
320
 
5.1%
716
 
4.1%
96
 
1.5%
54
 
1.0%
43
 
0.8%
82
 
0.5%
61
 
0.3%
Lowercase Letter
ValueCountFrequency (%)
n124
66.7%
a62
33.3%
Dash Punctuation
ValueCountFrequency (%)
-98
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common490
72.5%
Latin186
 
27.5%

Most frequent character per script

Common
ValueCountFrequency (%)
2134
27.3%
0131
26.7%
-98
20.0%
175
15.3%
320
 
4.1%
716
 
3.3%
96
 
1.2%
54
 
0.8%
43
 
0.6%
82
 
0.4%
Latin
ValueCountFrequency (%)
n124
66.7%
a62
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII676
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2134
19.8%
0131
19.4%
n124
18.3%
-98
14.5%
175
11.1%
a62
9.2%
320
 
3.0%
716
 
2.4%
96
 
0.9%
54
 
0.6%
Other values (3)6
 
0.9%

_embedded_show_officialSite
Categorical

HIGH CARDINALITY
HIGH CORRELATION
HIGH CORRELATION

Distinct63
Distinct (%)56.8%
Missing0
Missing (%)0.0%
Memory size1016.0 B
nan
12 
https://www.netflix.com/title/81087405
https://www.netflix.com/title/81002438
 
6
https://v.qq.com/x/cover/mzc00200tyfmlws.html
 
6
https://www.netflix.com/title/81075958
 
6
Other values (58)
73 

Length

Max length250
Median length79
Mean length43.10810811
Min length3

Characters and Unicode

Total characters4785
Distinct characters75
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique47 ?
Unique (%)42.3%

Sample

1st rowhttps://www.youtube.com/playlist?list=PLZ1FUdedsrSJubWkFFh5vKHzawzQk1mYI
2nd rowhttps://okko.tv/serial/zona-komforta
3rd rowhttps://start.ru/watch/passazhiry
4th rowhttps://v.qq.com/detail/w/ww18u675tfmhas6.html
5th rownan

Common Values

ValueCountFrequency (%)
nan12
 
10.8%
https://www.netflix.com/title/810874058
 
7.2%
https://www.netflix.com/title/810024386
 
5.4%
https://v.qq.com/x/cover/mzc00200tyfmlws.html6
 
5.4%
https://www.netflix.com/title/810759586
 
5.4%
https://www.netflix.com/title/810109655
 
4.5%
https://v.qq.com/x/search/?q=+%E4%BB%8A%E5%A4%95%E4%BD%95%E5%A4%95&stag=0&smartbox_ab=3
 
2.7%
https://v.qq.com/detail/m/mzc00200ur8p8zp.html2
 
1.8%
https://v.qq.com/detail/u/umpnsyqfu7f60se.html2
 
1.8%
https://v.qq.com/detail/m/mzc00200dnvb1wh.html2
 
1.8%
Other values (53)59
53.2%

Length

2022-05-09T21:24:12.439703image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
nan12
 
10.8%
https://www.netflix.com/title/810874058
 
7.2%
https://www.netflix.com/title/810024386
 
5.4%
https://v.qq.com/x/cover/mzc00200tyfmlws.html6
 
5.4%
https://www.netflix.com/title/810759586
 
5.4%
https://www.netflix.com/title/810109655
 
4.5%
https://v.qq.com/x/search/?q=+%e4%bb%8a%e5%a4%95%e4%bd%95%e5%a4%95&stag=0&smartbox_ab3
 
2.7%
https://www.iqiyi.com/a_19rrhllpip.html2
 
1.8%
http://www.iqiyi.com/a_19rrhvpyyp.html2
 
1.8%
https://www.gain.tv/t/21dn4vz5/10-bin-adim2
 
1.8%
Other values (53)59
53.2%

Most occurring characters

ValueCountFrequency (%)
t421
 
8.8%
/402
 
8.4%
w231
 
4.8%
.209
 
4.4%
s196
 
4.1%
e188
 
3.9%
o169
 
3.5%
m166
 
3.5%
h163
 
3.4%
i163
 
3.4%
Other values (65)2477
51.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter2966
62.0%
Other Punctuation847
 
17.7%
Decimal Number584
 
12.2%
Uppercase Letter312
 
6.5%
Dash Punctuation37
 
0.8%
Math Symbol25
 
0.5%
Connector Punctuation14
 
0.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t421
14.2%
w231
 
7.8%
s196
 
6.6%
e188
 
6.3%
o169
 
5.7%
m166
 
5.6%
h163
 
5.5%
i163
 
5.5%
l159
 
5.4%
p144
 
4.9%
Other values (16)966
32.6%
Uppercase Letter
ValueCountFrequency (%)
B48
15.4%
E43
13.8%
A26
 
8.3%
L20
 
6.4%
Y14
 
4.5%
W13
 
4.2%
D13
 
4.2%
P13
 
4.2%
H10
 
3.2%
R10
 
3.2%
Other values (16)102
32.7%
Decimal Number
ValueCountFrequency (%)
0129
22.1%
898
16.8%
172
12.3%
564
11.0%
952
8.9%
443
 
7.4%
240
 
6.8%
734
 
5.8%
626
 
4.5%
326
 
4.5%
Other Punctuation
ValueCountFrequency (%)
/402
47.5%
.209
24.7%
%111
 
13.1%
:99
 
11.7%
?13
 
1.5%
&9
 
1.1%
'2
 
0.2%
#1
 
0.1%
!1
 
0.1%
Math Symbol
ValueCountFrequency (%)
=22
88.0%
+3
 
12.0%
Dash Punctuation
ValueCountFrequency (%)
-37
100.0%
Connector Punctuation
ValueCountFrequency (%)
_14
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin3278
68.5%
Common1507
31.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
t421
 
12.8%
w231
 
7.0%
s196
 
6.0%
e188
 
5.7%
o169
 
5.2%
m166
 
5.1%
h163
 
5.0%
i163
 
5.0%
l159
 
4.9%
p144
 
4.4%
Other values (42)1278
39.0%
Common
ValueCountFrequency (%)
/402
26.7%
.209
13.9%
0129
 
8.6%
%111
 
7.4%
:99
 
6.6%
898
 
6.5%
172
 
4.8%
564
 
4.2%
952
 
3.5%
443
 
2.9%
Other values (13)228
15.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII4785
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
t421
 
8.8%
/402
 
8.4%
w231
 
4.8%
.209
 
4.4%
s196
 
4.1%
e188
 
3.9%
o169
 
3.5%
m166
 
3.5%
h163
 
3.4%
i163
 
3.4%
Other values (65)2477
51.8%

_embedded_show_weight
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct50
Distinct (%)45.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.23423423
Minimum0
Maximum99
Zeros8
Zeros (%)7.2%
Negative0
Negative (%)0.0%
Memory size1016.0 B
2022-05-09T21:24:12.549559image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q117
median29
Q359
95-th percentile88
Maximum99
Range99
Interquartile range (IQR)42

Descriptive statistics

Standard deviation29.32264249
Coefficient of variation (CV)0.7875183443
Kurtosis-0.9203412877
Mean37.23423423
Median Absolute Deviation (MAD)19
Skewness0.605760988
Sum4133
Variance859.8173628
MonotonicityNot monotonic
2022-05-09T21:24:12.659922image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
08
 
7.2%
408
 
7.2%
867
 
6.3%
887
 
6.3%
196
 
5.4%
186
 
5.4%
65
 
4.5%
365
 
4.5%
175
 
4.5%
84
 
3.6%
Other values (40)50
45.0%
ValueCountFrequency (%)
08
7.2%
11
 
0.9%
21
 
0.9%
31
 
0.9%
51
 
0.9%
65
4.5%
71
 
0.9%
84
3.6%
101
 
0.9%
143
 
2.7%
ValueCountFrequency (%)
991
 
0.9%
941
 
0.9%
887
6.3%
867
6.3%
841
 
0.9%
821
 
0.9%
772
 
1.8%
731
 
0.9%
701
 
0.9%
691
 
0.9%

_embedded_show_dvdCountry
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Memory size1016.0 B
nan
109 
{'name': 'Korea, Republic of', 'code': 'KR', 'timezone': 'Asia/Seoul'}
 
1
{'name': 'Ukraine', 'code': 'UA', 'timezone': 'Europe/Zaporozhye'}
 
1

Length

Max length70
Median length3
Mean length4.171171171
Min length3

Characters and Unicode

Total characters463
Distinct characters34
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)1.8%

Sample

1st rownan
2nd rownan
3rd rownan
4th rownan
5th rownan

Common Values

ValueCountFrequency (%)
nan109
98.2%
{'name': 'Korea, Republic of', 'code': 'KR', 'timezone': 'Asia/Seoul'}1
 
0.9%
{'name': 'Ukraine', 'code': 'UA', 'timezone': 'Europe/Zaporozhye'}1
 
0.9%

Length

2022-05-09T21:24:12.784930image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-09T21:24:12.894427image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
nan109
88.6%
name2
 
1.6%
code2
 
1.6%
timezone2
 
1.6%
korea1
 
0.8%
republic1
 
0.8%
of1
 
0.8%
kr1
 
0.8%
asia/seoul1
 
0.8%
ukraine1
 
0.8%
Other values (2)2
 
1.6%

Most occurring characters

ValueCountFrequency (%)
n223
48.2%
a115
24.8%
'24
 
5.2%
e14
 
3.0%
12
 
2.6%
o10
 
2.2%
:6
 
1.3%
,5
 
1.1%
i5
 
1.1%
r4
 
0.9%
Other values (24)45
 
9.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter399
86.2%
Other Punctuation37
 
8.0%
Space Separator12
 
2.6%
Uppercase Letter11
 
2.4%
Close Punctuation2
 
0.4%
Open Punctuation2
 
0.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n223
55.9%
a115
28.8%
e14
 
3.5%
o10
 
2.5%
i5
 
1.3%
r4
 
1.0%
m4
 
1.0%
p3
 
0.8%
u3
 
0.8%
c3
 
0.8%
Other values (10)15
 
3.8%
Uppercase Letter
ValueCountFrequency (%)
K2
18.2%
U2
18.2%
A2
18.2%
R2
18.2%
S1
9.1%
E1
9.1%
Z1
9.1%
Other Punctuation
ValueCountFrequency (%)
'24
64.9%
:6
 
16.2%
,5
 
13.5%
/2
 
5.4%
Space Separator
ValueCountFrequency (%)
12
100.0%
Close Punctuation
ValueCountFrequency (%)
}2
100.0%
Open Punctuation
ValueCountFrequency (%)
{2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin410
88.6%
Common53
 
11.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
n223
54.4%
a115
28.0%
e14
 
3.4%
o10
 
2.4%
i5
 
1.2%
r4
 
1.0%
m4
 
1.0%
p3
 
0.7%
u3
 
0.7%
c3
 
0.7%
Other values (17)26
 
6.3%
Common
ValueCountFrequency (%)
'24
45.3%
12
22.6%
:6
 
11.3%
,5
 
9.4%
}2
 
3.8%
/2
 
3.8%
{2
 
3.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII463
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n223
48.2%
a115
24.8%
'24
 
5.2%
e14
 
3.0%
12
 
2.6%
o10
 
2.2%
:6
 
1.3%
,5
 
1.1%
i5
 
1.1%
r4
 
0.9%
Other values (24)45
 
9.7%

_embedded_show_summary
Categorical

HIGH CARDINALITY
HIGH CORRELATION
HIGH CORRELATION

Distinct62
Distinct (%)55.9%
Missing0
Missing (%)0.0%
Memory size1016.0 B
nan
<p>The kings &amp; queens of leftover cooking will take a leftover dish on an epic journey across two rounds. Each half-hour after party, our home cooks will compete in the ultimate food makeover, finding ways to give old leftovers new life, all in the hopes of winning a $10,000 prize! Join host Jackie Tohn and judges David So and Rosemary Shrager as they watch our contestants transform leftovers into delicious creations. </p>
<p><b>Equinox</b> is a character-driven supernatural thriller about a young woman named Astrid, who is affected by the unexplainable disappearance of a school class in 1999. The series is set in Denmark and swipes back and forth between 1999, where it all started, and the present time.</p><p>Astrid is only 10 years old in the year 1999 when a class of graduating students inexplicably disappears without a trace. Astrid, who was close to one of the missing students becomes traumatized and plagued by horrific visions after the disappearance. In 2020, Astrid is peacefully living with her family when all of a sudden the nightmares come back and start haunting her. When the one survivor from 1999 mysteriously dies, Astrid is determined to find out what happened to the class, only to discover a dark and unsettling truth that involves her in ways she never imagined.</p>
 
6
<p>Young noble Chu Yun Xiao crosses paths with female doctor Leng Xing Chen because of a beauty portrait. Together with their friends, the six people who are pulled into a terrifying conspiracy form a detective team to uncover the secrets surrounding the portrait. Chu Yunxiao has ventured into the pugilistic world for the first time. Aspiring to be a chivalrous hero, he relies on his outstanding martial arts skills and high intelligence to solve a difficult case. However, he unexpectedly discovers that he is also a chess piece in this dangerous game. As the fog is slowly lifted, Chu Yun Xiao becomes aware of an unbearable truth that the past twenty years of his life was nothing but a lie.</p>
 
6
<p><b>Transformers: War for Cybertron Trilogy</b> is a three-part arc following the war between the Autobots and Decepticons, complete with a new animation look and style.</p>
 
6
Other values (57)
76 

Length

Max length1253
Median length682
Mean length379.6936937
Min length3

Characters and Unicode

Total characters42146
Distinct characters89
Distinct categories11 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique42 ?
Unique (%)37.8%

Sample

1st rownan
2nd rownan
3rd rownan
4th row<p>One will to create oceans. One will to summon the mulberry fields.<br /><br />One will to slaughter countless devils. One will to eradicate innumerable immortals.<br /><br />Only my will… is eternal.<br /><br />A Will Eternal tells the tale of Bai Xiaochun, an endearing but exasperating young man who is driven primarily by his fear of death and desire to live forever, but who deeply values friendship and family.<br /><br />(Source: Novel Updates)</p>
5th row<p>Da Eun works part-time and Kim Byul is an idol in her 5th years since debut. These two girls who look alike decide to change each other's lives just for 7 days. It tells the romantic encounters of these 2 girls.</p>

Common Values

ValueCountFrequency (%)
nan9
 
8.1%
<p>The kings &amp; queens of leftover cooking will take a leftover dish on an epic journey across two rounds. Each half-hour after party, our home cooks will compete in the ultimate food makeover, finding ways to give old leftovers new life, all in the hopes of winning a $10,000 prize! Join host Jackie Tohn and judges David So and Rosemary Shrager as they watch our contestants transform leftovers into delicious creations. </p>8
 
7.2%
<p><b>Equinox</b> is a character-driven supernatural thriller about a young woman named Astrid, who is affected by the unexplainable disappearance of a school class in 1999. The series is set in Denmark and swipes back and forth between 1999, where it all started, and the present time.</p><p>Astrid is only 10 years old in the year 1999 when a class of graduating students inexplicably disappears without a trace. Astrid, who was close to one of the missing students becomes traumatized and plagued by horrific visions after the disappearance. In 2020, Astrid is peacefully living with her family when all of a sudden the nightmares come back and start haunting her. When the one survivor from 1999 mysteriously dies, Astrid is determined to find out what happened to the class, only to discover a dark and unsettling truth that involves her in ways she never imagined.</p>6
 
5.4%
<p>Young noble Chu Yun Xiao crosses paths with female doctor Leng Xing Chen because of a beauty portrait. Together with their friends, the six people who are pulled into a terrifying conspiracy form a detective team to uncover the secrets surrounding the portrait. Chu Yunxiao has ventured into the pugilistic world for the first time. Aspiring to be a chivalrous hero, he relies on his outstanding martial arts skills and high intelligence to solve a difficult case. However, he unexpectedly discovers that he is also a chess piece in this dangerous game. As the fog is slowly lifted, Chu Yun Xiao becomes aware of an unbearable truth that the past twenty years of his life was nothing but a lie.</p>6
 
5.4%
<p><b>Transformers: War for Cybertron Trilogy</b> is a three-part arc following the war between the Autobots and Decepticons, complete with a new animation look and style.</p>6
 
5.4%
<p>Amidst a heroin crisis, Vincenzo Muccioli cared for the addicted, earning him fierce public devotion — even as charges of violence began to mount.</p>5
 
4.5%
<p>During the Yin Dynasty, Dong Yue, a brave general in the Dingyuan Rebellion, was sent back in time to stop a war that would claim the lives of countless innocents. She sets out to murder corrupted officer Lu Yuantong in an attempt to prevent war, and during her journey she met Feng Xi and Pang Yu. Pang Yu and Feng Xi were old friends who cared deeply for each other, but fell out and turn into enemies. While trying to reconcile the two brothers, Dong Yue also tries to stop Lu Yuantang's evil schemes which are poised to tear the nation apart with their help.</p>3
 
2.7%
<p>‎At the end of the 20th century, due to the sudden decision of Xin Shensheng, gao Shan's business went bankrupt. Gao Shan wants to prove his father's innocence, but on his way suddenly falls in love with the daughter of Xin Shensheng, Tsin Waugh. Learning about the intentions of Gao Shan, Xin Shansheng makes him quit his job. ‎<br /><br />‎Gao Shan decides to go to Hong Kong to start from scratch, where he meets a benefactor and earns his first million in his life. Under the guidance of a mentor, he goes to Beijing and becomes a well-known investor. Soon Gao Shan meets Tsin Vo, who became a financial headhunter. Can love help them find their way to each other again? ‎<br /><br />‎Based on the novel by Xiao Moli "Little Storm 1.0"‎</p>2
 
1.8%
<p>Two unlikely individuals join forces to find the truth behind a series of murders using an unconventional method. Chen Si, a female detective with a sense of justice, unexpectedly becomess partners with Yuan Shuai, a dream interpreter with a dark past.</p>2
 
1.8%
<p>Princess Ming Yue and Li Qiang, the emperor's ninth prince, are forced to marry in order to keep the peace in their kingdoms. As the princess finally seems to be getting used to her new life in Chang'An (an ancient Chinese capital), there are plots hovering against her and the royal family.</p>2
 
1.8%
Other values (52)62
55.9%

Length

2022-05-09T21:24:13.004208image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
the366
 
5.2%
a253
 
3.6%
and225
 
3.2%
to199
 
2.8%
of177
 
2.5%
in130
 
1.8%
is92
 
1.3%
with74
 
1.1%
her65
 
0.9%
his64
 
0.9%
Other values (1615)5387
76.6%

Most occurring characters

ValueCountFrequency (%)
6904
16.4%
e3943
 
9.4%
t2647
 
6.3%
a2609
 
6.2%
n2440
 
5.8%
o2377
 
5.6%
i2373
 
5.6%
s2160
 
5.1%
r2068
 
4.9%
h1640
 
3.9%
Other values (79)12985
30.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter32055
76.1%
Space Separator6926
 
16.4%
Uppercase Letter1154
 
2.7%
Other Punctuation997
 
2.4%
Math Symbol660
 
1.6%
Decimal Number240
 
0.6%
Dash Punctuation82
 
0.2%
Format12
 
< 0.1%
Currency Symbol8
 
< 0.1%
Open Punctuation6
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e3943
12.3%
t2647
 
8.3%
a2609
 
8.1%
n2440
 
7.6%
o2377
 
7.4%
i2373
 
7.4%
s2160
 
6.7%
r2068
 
6.5%
h1640
 
5.1%
l1259
 
3.9%
Other values (22)8539
26.6%
Uppercase Letter
ValueCountFrequency (%)
S119
 
10.3%
A110
 
9.5%
T97
 
8.4%
W82
 
7.1%
Y75
 
6.5%
C70
 
6.1%
L65
 
5.6%
D58
 
5.0%
M55
 
4.8%
X47
 
4.1%
Other values (16)376
32.6%
Other Punctuation
ValueCountFrequency (%)
,364
36.5%
.332
33.3%
/175
17.6%
'51
 
5.1%
"21
 
2.1%
!15
 
1.5%
:13
 
1.3%
?9
 
0.9%
&8
 
0.8%
;8
 
0.8%
Decimal Number
ValueCountFrequency (%)
976
31.7%
071
29.6%
151
21.2%
228
 
11.7%
85
 
2.1%
33
 
1.2%
53
 
1.2%
72
 
0.8%
41
 
0.4%
Dash Punctuation
ValueCountFrequency (%)
-64
78.0%
16
 
19.5%
2
 
2.4%
Space Separator
ValueCountFrequency (%)
6904
99.7%
 22
 
0.3%
Math Symbol
ValueCountFrequency (%)
>330
50.0%
<330
50.0%
Format
ValueCountFrequency (%)
12
100.0%
Currency Symbol
ValueCountFrequency (%)
$8
100.0%
Open Punctuation
ValueCountFrequency (%)
(6
100.0%
Close Punctuation
ValueCountFrequency (%)
)6
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin33209
78.8%
Common8937
 
21.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
e3943
11.9%
t2647
 
8.0%
a2609
 
7.9%
n2440
 
7.3%
o2377
 
7.2%
i2373
 
7.1%
s2160
 
6.5%
r2068
 
6.2%
h1640
 
4.9%
l1259
 
3.8%
Other values (48)9693
29.2%
Common
ValueCountFrequency (%)
6904
77.3%
,364
 
4.1%
.332
 
3.7%
>330
 
3.7%
<330
 
3.7%
/175
 
2.0%
976
 
0.9%
071
 
0.8%
-64
 
0.7%
151
 
0.6%
Other values (21)240
 
2.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII42085
99.9%
Punctuation31
 
0.1%
None30
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
6904
16.4%
e3943
 
9.4%
t2647
 
6.3%
a2609
 
6.2%
n2440
 
5.8%
o2377
 
5.6%
i2373
 
5.6%
s2160
 
5.1%
r2068
 
4.9%
h1640
 
3.9%
Other values (68)12924
30.7%
None
ValueCountFrequency (%)
 22
73.3%
á2
 
6.7%
č2
 
6.7%
é1
 
3.3%
ū1
 
3.3%
ė1
 
3.3%
å1
 
3.3%
Punctuation
ValueCountFrequency (%)
16
51.6%
12
38.7%
2
 
6.5%
1
 
3.2%

_embedded_show_updated
Real number (ℝ≥0)

HIGH CORRELATION

Distinct70
Distinct (%)63.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1632002746
Minimum1609402718
Maximum1652004050
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1016.0 B
2022-05-09T21:24:13.225163image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1609402718
5-th percentile1609468930
Q11613214909
median1640453062
Q31649674086
95-th percentile1651839506
Maximum1652004050
Range42601332
Interquartile range (IQR)36459176.5

Descriptive statistics

Standard deviation17515769.21
Coefficient of variation (CV)0.01073268366
Kurtosis-1.846656545
Mean1632002746
Median Absolute Deviation (MAD)11410204
Skewness-0.1390105365
Sum1.811523049 × 1011
Variance3.068021709 × 1014
MonotonicityNot monotonic
2022-05-09T21:24:13.334280image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
16132149098
 
7.2%
16506226516
 
5.4%
16461207346
 
5.4%
16094027186
 
5.4%
16137543075
 
4.5%
16095351413
 
2.7%
16481900582
 
1.8%
16124781452
 
1.8%
16150585542
 
1.8%
16105066872
 
1.8%
Other values (60)69
62.2%
ValueCountFrequency (%)
16094027186
5.4%
16095351413
2.7%
16096516762
 
1.8%
16097998962
 
1.8%
16105066872
 
1.8%
16111891791
 
0.9%
16123781171
 
0.9%
16124781452
 
1.8%
16124799201
 
0.9%
16128425832
 
1.8%
ValueCountFrequency (%)
16520040501
0.9%
16519907551
0.9%
16519339621
0.9%
16518632662
1.8%
16518403651
0.9%
16518386471
0.9%
16517491651
0.9%
16516880411
0.9%
16516821881
0.9%
16516456841
0.9%

_links_self_href
Categorical

HIGH CARDINALITY
UNIFORM
UNIQUE

Distinct111
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size1016.0 B
https://api.tvmaze.com/episodes/1977902
 
1
https://api.tvmaze.com/episodes/1949329
 
1
https://api.tvmaze.com/episodes/2042003
 
1
https://api.tvmaze.com/episodes/1996399
 
1
https://api.tvmaze.com/episodes/1955318
 
1
Other values (106)
106 

Length

Max length39
Median length39
Mean length39
Min length39

Characters and Unicode

Total characters4329
Distinct characters26
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique111 ?
Unique (%)100.0%

Sample

1st rowhttps://api.tvmaze.com/episodes/1977902
2nd rowhttps://api.tvmaze.com/episodes/2015818
3rd rowhttps://api.tvmaze.com/episodes/1964000
4th rowhttps://api.tvmaze.com/episodes/1995405
5th rowhttps://api.tvmaze.com/episodes/2007760

Common Values

ValueCountFrequency (%)
https://api.tvmaze.com/episodes/19779021
 
0.9%
https://api.tvmaze.com/episodes/19493291
 
0.9%
https://api.tvmaze.com/episodes/20420031
 
0.9%
https://api.tvmaze.com/episodes/19963991
 
0.9%
https://api.tvmaze.com/episodes/19553181
 
0.9%
https://api.tvmaze.com/episodes/19967861
 
0.9%
https://api.tvmaze.com/episodes/19493361
 
0.9%
https://api.tvmaze.com/episodes/19493351
 
0.9%
https://api.tvmaze.com/episodes/19493341
 
0.9%
https://api.tvmaze.com/episodes/19493331
 
0.9%
Other values (101)101
91.0%

Length

2022-05-09T21:24:13.428114image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
https://api.tvmaze.com/episodes/19779021
 
0.9%
https://api.tvmaze.com/episodes/20654471
 
0.9%
https://api.tvmaze.com/episodes/19640001
 
0.9%
https://api.tvmaze.com/episodes/19954051
 
0.9%
https://api.tvmaze.com/episodes/20077601
 
0.9%
https://api.tvmaze.com/episodes/19857891
 
0.9%
https://api.tvmaze.com/episodes/20396221
 
0.9%
https://api.tvmaze.com/episodes/20396231
 
0.9%
https://api.tvmaze.com/episodes/23244271
 
0.9%
https://api.tvmaze.com/episodes/23244281
 
0.9%
Other values (101)101
91.0%

Most occurring characters

ValueCountFrequency (%)
/444
 
10.3%
p333
 
7.7%
s333
 
7.7%
e333
 
7.7%
t333
 
7.7%
o222
 
5.1%
a222
 
5.1%
i222
 
5.1%
.222
 
5.1%
m222
 
5.1%
Other values (16)1443
33.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter2775
64.1%
Other Punctuation777
 
17.9%
Decimal Number777
 
17.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
p333
12.0%
s333
12.0%
e333
12.0%
t333
12.0%
o222
8.0%
a222
8.0%
i222
8.0%
m222
8.0%
h111
 
4.0%
d111
 
4.0%
Other values (3)333
12.0%
Decimal Number
ValueCountFrequency (%)
9120
15.4%
2116
14.9%
0100
12.9%
199
12.7%
367
8.6%
665
8.4%
863
8.1%
753
6.8%
450
6.4%
544
 
5.7%
Other Punctuation
ValueCountFrequency (%)
/444
57.1%
.222
28.6%
:111
 
14.3%

Most occurring scripts

ValueCountFrequency (%)
Latin2775
64.1%
Common1554
35.9%

Most frequent character per script

Common
ValueCountFrequency (%)
/444
28.6%
.222
14.3%
9120
 
7.7%
2116
 
7.5%
:111
 
7.1%
0100
 
6.4%
199
 
6.4%
367
 
4.3%
665
 
4.2%
863
 
4.1%
Other values (3)147
 
9.5%
Latin
ValueCountFrequency (%)
p333
12.0%
s333
12.0%
e333
12.0%
t333
12.0%
o222
8.0%
a222
8.0%
i222
8.0%
m222
8.0%
h111
 
4.0%
d111
 
4.0%
Other values (3)333
12.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII4329
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
/444
 
10.3%
p333
 
7.7%
s333
 
7.7%
e333
 
7.7%
t333
 
7.7%
o222
 
5.1%
a222
 
5.1%
i222
 
5.1%
.222
 
5.1%
m222
 
5.1%
Other values (16)1443
33.3%

Interactions

2022-05-09T21:24:05.023858image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:23:42.803871image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:23:48.336704image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:23:50.728967image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:23:52.941851image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:23:55.181373image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:23:59.150518image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:24:00.580917image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:24:02.805711image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:24:06.032297image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:23:44.197570image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:23:49.445096image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:23:51.707756image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:23:53.945427image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:23:56.332255image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:23:59.701398image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:24:01.563019image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:24:03.764440image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:24:06.138005image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:23:44.663162image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:23:49.548549image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:23:51.809795image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:23:54.044906image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:23:56.619500image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:23:59.801248image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:24:01.662961image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:24:03.863391image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:24:06.238624image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:23:45.188688image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:23:49.662553image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:23:51.903640image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:23:54.137747image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:23:56.950642image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:23:59.893951image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:24:01.762024image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:24:03.958681image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:24:06.331635image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:23:45.620332image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:23:49.781179image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:23:51.999295image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:23:54.224062image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:23:57.237308image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:23:59.988212image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:24:01.861397image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:24:04.075205image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:24:06.887505image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:23:46.660433image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:23:50.336830image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:23:52.568823image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:23:54.786180image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:23:58.099773image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:24:00.187860image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:24:02.367066image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:24:04.633269image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:24:06.982523image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:23:46.955387image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:23:50.433464image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:23:52.667641image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:23:54.883329image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:23:58.264594image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:24:00.298229image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:24:02.465353image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:24:04.727338image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:24:07.075456image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:23:47.394890image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:23:50.528149image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:23:52.761524image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:23:54.988619image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:23:58.535549image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:24:00.401714image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:24:02.582062image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:24:04.823666image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:24:07.188257image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:23:47.865018image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:23:50.622750image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:23:52.864133image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:23:55.081388image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:23:58.825746image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:24:00.487131image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:24:02.696094image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:24:04.930732image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Correlations

2022-05-09T21:24:13.506529image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-05-09T21:24:13.647084image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-05-09T21:24:13.786892image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-05-09T21:24:13.953661image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-05-09T21:24:14.157428image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-05-09T21:24:07.362942image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
A simple visualization of nullity by column.
2022-05-09T21:24:08.064257image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2022-05-09T21:24:08.281181image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2022-05-09T21:24:08.397981image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

idurlnameseasonnumbertypeairdateairtimeairstampruntimesummary_embedded_show_id_embedded_show_url_embedded_show_name_embedded_show_type_embedded_show_language_embedded_show_genres_embedded_show_status_embedded_show_runtime_embedded_show_averageRuntime_embedded_show_premiered_embedded_show_ended_embedded_show_officialSite_embedded_show_weight_embedded_show_dvdCountry_embedded_show_summary_embedded_show_updated_links_self_href
02179615https://www.tvmaze.com/episodes/2179615/kontakty-1x32-kontakty-v-telefone-arsenia-popova-pavel-vola-ekaterina-varnava-ila-sobolev-edgard-zapasnyjКОНТАКТЫ в телефоне Арсения Попова: Павел Воля, Екатерина Варнава, Илья Соболев, Эдгард Запашный1.032.0regular2020-12-3012:002020-12-30T00:00:00+00:0045.0nan49630https://www.tvmaze.com/shows/49630/kontaktyКонтактыGame ShowRussian[]Running30.041.02019-04-03nanhttps://www.youtube.com/playlist?list=PLZ1FUdedsrSJubWkFFh5vKHzawzQk1mYI53.0nannan1.651688e+09https://api.tvmaze.com/episodes/1977902
12001718https://www.tvmaze.com/episodes/2001718/zona-komforta-s01-special-zona-komforta-osuitelno-specialnyj-vypuskЗона Комфорта. Ошуительно Специальный Выпуск1.0NaNinsignificant_special2020-12-30nan2020-12-30T00:00:00+00:0015.0<p>A unique release of The Shocking Howe. Actors and writers of the "Comfort Zone" series will tell you how the project was created. Of course, everything is in the style of the Shocking Howe.</p>51510https://www.tvmaze.com/shows/51510/zona-komfortaЗона комфортаScriptedRussian['Comedy']EndedNaN29.02020-10-222022-03-02https://okko.tv/serial/zona-komforta42.0nannan1.649573e+09https://api.tvmaze.com/episodes/2015818
21987721https://www.tvmaze.com/episodes/1987721/passaziry-1x03-kirillКирилл1.03.0regular2020-12-30nan2020-12-30T00:00:00+00:0020.0nan52499https://www.tvmaze.com/shows/52499/passaziryПассажирыScriptedRussian['Drama', 'Supernatural']Running22.023.02020-12-24nanhttps://start.ru/watch/passazhiry84.0nannan1.651991e+09https://api.tvmaze.com/episodes/1964000
32095630https://www.tvmaze.com/episodes/2095630/yi-nian-yong-heng-1x23-episode-23Episode 231.023.0regular2020-12-3010:002020-12-30T02:00:00+00:0019.0nan49652https://www.tvmaze.com/shows/49652/yi-nian-yong-hengYi Nian Yong HengAnimationChinese['Comedy', 'Action', 'Anime', 'Fantasy']Running19.019.02020-08-12nanhttps://v.qq.com/detail/w/ww18u675tfmhas6.html17.0nan<p>One will to create oceans. One will to summon the mulberry fields.<br /><br />One will to slaughter countless devils. One will to eradicate innumerable immortals.<br /><br />Only my will… is eternal.<br /><br />A Will Eternal tells the tale of Bai Xiaochun, an endearing but exasperating young man who is driven primarily by his fear of death and desire to live forever, but who deeply values friendship and family.<br /><br />(Source: Novel Updates)</p>1.649494e+09https://api.tvmaze.com/episodes/1995405
41993659https://www.tvmaze.com/episodes/1993659/7-days-of-romance-2x04-episode-4Episode 42.04.0regular2020-12-30nan2020-12-30T03:00:00+00:0015.0nan44276https://www.tvmaze.com/shows/44276/7-days-of-romance7 Days of RomanceScriptedKorean['Drama', 'Romance']EndedNaN15.02019-10-082021-01-20nan82.0nan<p>Da Eun works part-time and Kim Byul is an idol in her 5th years since debut. These two girls who look alike decide to change each other's lives just for 7 days. It tells the romantic encounters of these 2 girls.</p>1.650034e+09https://api.tvmaze.com/episodes/2007760
52096304https://www.tvmaze.com/episodes/2096304/no-turning-back-romance-1x08-881.08.0regular2020-12-30nan2020-12-30T03:00:00+00:0012.0nan55002https://www.tvmaze.com/shows/55002/no-turning-back-romanceNo Turning Back RomanceScriptedKorean[]EndedNaN12.02020-12-082021-01-06nan20.0nan<p>A teen romance of So Dam, a sixteen-year-old girl, who has never dated before but she receives her first-ever love confession from a mysterious boy. She is looking for the boy who secretly confessed to her while she was asleep on her desk. The clues include a male voice, mango fruit scent and gym uniform. She must piece the puzzle to find that person among the likely candidates that include hot shots Park Ji Hoo, Jeong Han Kyul, and Joo In Hyuk.</p>1.621617e+09https://api.tvmaze.com/episodes/1985789
62324425https://www.tvmaze.com/episodes/2324425/unique-lady-2x13-episode-13Episode 132.013.0regular2020-12-3012:002020-12-30T04:00:00+00:0040.0nan41490https://www.tvmaze.com/shows/41490/unique-ladyUnique LadyScriptedChinese['Drama', 'Comedy', 'Romance']Ended38.042.02019-01-172021-01-07http://www.iqiyi.com/a_19rrhvpyyp.html68.0nan<p>Lin Luo Jing accidentally gets drawn into a game world where she is the daughter of the prime minister and meets all kind of beautiful men with different personalities. Among them are a sword deity, an imperial bodyguard, a playful rich man and an arrogant prince. The system informs her that she can only return to the real world after she finds her true love. While there seems tobe an abundance of good men around Luo Jing, there is one man she can't stand at all: the prince of the barbarian Yuan Kingdom Zhong Wu Mei. But out of all men, she ends up in an arranged marriage with Wu Mei.</p><p>Thus begins their love-hate relationship and her journey to find true love in order to win the game.</p>1.651863e+09https://api.tvmaze.com/episodes/2039622
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